Category: Knowledge

Accelerating Consumer Decarbonization: Prioritizing Investments to Maximize Impact Return

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As temperatures rise and climate change has an increasingly severe impact on human life, individuals and institutions alike are becoming more concerned regarding greenhouse gas emissions and the adoption of technologies which can decarbonize the world.  While the majority of greenhouse gas emissions is generated by industrial and commercial activity[1] and the decarbonization of one individual’s life will have minimal impact on the world’s carbon footprint, it is still important to consider the decarbonization of consumer life.  Not only is there overlap between the technologies that can decarbonize consumer life and commercial activities, but corporations serve the end consumer – shifting consumer demand away from high emissions goods and services will incentivize corporations to boost supply of sustainable technology.  Increased consumer decarbonization may lead to a positive feedback loop with consumers placing more pressure on corporations to decarbonize and governments to drive policies supporting environmental sustainability.

Where should investors look to maximize the impact of their dollars on decarbonizing consumer life?  What will drive consumers to opt for sustainable solutions, and how are ecosystem players such as startups, governments, and financial institutions working to accelerate consumer adoption of green technology?  This article will explore the state of the world and the potential solutions for speeding up the voluntary decarbonization of consumer life.

 

State of the World: Consumer Emissions

Research shows that the major sources of emissions in consumer life are Transportation and Housing, followed by Food as a relatively distant third major source of emissions.[2]

Total carbon footprint of the typical U.S. household: 48 t CO2e/yr.  Blue indicates direct emissions, green indicates indirect emissions.  Source: “Quantifying Carbon Footprint Reduction Opportunities for U.S. Households and Communities”, 2010

As shown in the figure above, the single largest source of direct consumer emissions is motor vehicle fuel.  While this figure may vary in different parts of the world (particularly those with a less dominant car culture than the US), it is apparent that the most impactful way to decarbonize consumer life is to minimize the use of traditional ICE vehicles, either through increased adoption of public transportation or adoption of electric vehicles.

Consumers generate emissions from Housing in the form of electricity usage; as electricity grids in most countries are still driven by fossil fuels, this generates indirect emissions in consumer life.  Removing emissions from this sector therefore means reducing energy consumption from the grid via residential solar installation.  Further, reducing total energy consumption by the consumer will automatically reduce consumption from the grid.  The primary usage of electricity in residential buildings is related to Heating, Ventilation, and Air Conditioning (HVAC) systems for heating and cooling buildings.  Therefore, consumers can further reduce emissions by installing energy efficiency tools or optimizing home insulation (allowing HVAC systems to operate more efficiently).

While the data above is focused on the US market, there are similar findings throughout the rest of the world.  Studies in the European market also look at housing, mobility and food as the key emissions sectors for policy makers to tackle.  The importance of energy and housing in consumer emissions is even more prevalent in developing markets and low-income households as the bulk of these emissions are driven by energy consumption.[3]  Despite the availability of green alternatives for transportation and housing, adoption by consumers has been limited.  EVs only accounted for 12% of US passenger vehicle sales in 2023[4]; only 5% of US homes have rooftop solar installed.[5]  In recent years, the rate of adoption has improved; the next sections will discuss the driving factors that can contribute to increased adoption as well as various approaches by governments, startups, and financial institutions which will continue to accelerate consumer decarbonization.

 

How to Accelerate Consumer Adoption of Sustainable Technology

Given the emissions sources identified above, what is the key to decarbonizing consumer life?  While carbon credits are often cited as a market mechanism for decarbonization, the uncertainty surrounding their value and effectiveness makes carbon credits a poor option for driving consumer decarbonization.  On the individual scale, carbon credits provide insufficient financial incentive (if any), and alternatives such as green reward tokens have limited use and may not be particularly useful to the average consumer.  Unlike the corporate sector, there are also no regulations enforcing consumers to decarbonize their lives, thus all consumer decarbonization is voluntary.

For consumer transportation and housing, much of the technology required for decarbonization is already available and proven to work, but adoption of these technologies has been slow to materialize.  Despite multiple reports indicating the majority of consumers are concerned about sustainability and are looking to adopt sustainable technology and products, concerns around additional costs of sustainable living may prevent rapid adoption.[6]  As will be discussed below, it is also often the case that choosing the sustainable option will not result in higher cost of living.  In these instances, if consumers are still slow to make the choice to decarbonize, it suggests that there are still non-financial barriers to adoption that must be overcome.  In considering why consumers have been slow to adopt sustainable technology and how governments and businesses can improve adoption, it is useful to think about the triggers for changes in human behavior (such as internal motivations, financial considerations, or simply removal of friction).  This consideration leads to the assumption that voluntary decarbonization by consumers will require at least one of the following conditions: a shift in consumer mindset, a financial incentive, and/or sufficient quality and access to sustainable alternatives.

The required shift in consumer mindset is for consumers to see sustainability as necessary to the point that they are willing to make sacrifices or pay a green premium to adopt sustainable technologies.  By definition, this is a willingness to adopt new technology prior to price parity.  This kind of radical shift in mindset is difficult to control or implement, and likely will take a long time (and possibly an extreme and harmful crisis to trigger a true shift in mindset).  Further, trying to push consumers to pay a premium for green technologies also presents problems with respect to income inequality – many consumers in developing markets do not have the financial resources to pay any kind of premium.

Financial incentives in this framework refer to any mechanism that makes the adoption of sustainable technology cheaper than the traditional alternative.  This includes direct subsidies or tax incentives, as well as financing models that can alter payment horizons.  In the context of this framework, “financial incentives” also includes technological improvements which can decrease the cost of production of green goods or services below their non-sustainable counterparts.

Sufficient quality and access to sustainable alternatives may be hard to quantify, but for the purpose of this framework, “sufficient” means enough to enable consumers to willingly adopt technology to decarbonize their lives when the “green” and “not green” options are equal in price.  The assumption that consumers will adopt at price parity (whether that parity is achieved by technological/production improvements to decrease the intrinsic cost or through the aforementioned financial incentives) ignores the non-monetary and unquantifiable aspects of a product or technology.  For example, switching to public transportation instead of driving is likely to save a consumer money (and certainly is unlikely to require them to pay a green premium), however in many cases switching to public transportation may increase the difficulty for an individual to reach their intended destination.  Alternatively, green solutions may be equivalent in price, but consumers may lack awareness of how to access the relevant subsidies or lack understanding of what solutions to implement (for example, what products improve home insulation and therefore lead to lower HVAC energy usage).  “Sufficient quality and access to sustainable alternatives” is therefore meant to improvements in technology or business models which can reduce these non-financial barriers to adoption.

While a shift in consumer mindset may be necessary in the long run to drive full decarbonization of consumer life, it is clear that in the short run, ecosystem players hoping to drive consumer decarbonization should be focusing on how to improve financial incentives, quality and access to decarbonization technologies, or both.  This can be achieved by reducing the financial barriers through government subsidies or innovative financing models, or by improving the access to or useability of green technology.

 

How Are Ecosystem Players Tackling Consumer Decarbonization?

Using the framework above, effective models for boosting consumer adoption of sustainable technology can be divided into two categories: models for providing financial incentives or reducing the financial barriers to adoption, and businesses aimed at improving the access to and useability of decarbonization technology.  Initiatives to support consumer decarbonization can come from across the ecosystem. It may appear that governments and financial institutions are best placed to focus on financial incentives while startups and technology companies focus on improving access and useability, however, as will be demonstrated below, there are opportunities for all of these players in both categories.  It is also important to note that financial incentives alone may not be enough – the most effective models often combine financial incentives with frictionless access/useability.

Financial Incentive Models

Governments are typically the best (if not only) entity for providing direct financial incentives.  Decarbonization creates a public good, and if there is an increased cost for individuals to adopt sustainable technology, governments are the right entity to cover any increased costs to encourage faster adoption by individuals.  There are many examples around the world of governments stepping in with policy initiatives designed to financially incentivize consumers to look for sustainable options.  The US Inflation Reduction Act provides a federal tax credit for 30% of the cost of residential solar installation.[7]  In Thailand, the government has allocated 7.12 billion THB to fund its EV subsidy program.[8]  The EV 3.5 scheme provides consumer subsidies for EV car purchases of up to 100,000 THB (depending on the vehicle’s battery size and the year of purchase), as well as decreasing excise taxes (from 8% down to 2%) and import duties (by up to 40%) in order to make EV adoption more financially attractive for consumers.[9]

While subsidies are the simplest form of financial incentives to understand, efforts to financially incentivize voluntary consumer decarbonization are not limited to governments; financial institutions and technology startups are also finding new financing models to encourage consumers to adopt sustainability.  Banks and solar installers can collaborate to provide consumers with different financing options such as solar leases or power purchase agreements (PPA) to decrease the upfront costs of solar installation.  In Southeast Asia, technology startups like Helios and Okapi work with partners across the residential solar and financial supply chain to enable homebuyers to access affordable solar installation.  On the other end of the cost-benefit spectrum, companies like Powerledger are leveraging blockchain technology to enable P2P energy trading not only for businesses, but within residential communities.[10]  At scale, P2P energy trading platforms could enable consumers to generate additional future income from their solar panels, providing more attractive returns in exchange for the upfront cost of adopting solar technology in their homes.

Models to Improve Access and Useability

The other significant opportunity to decarbonize consumer life lies in improving consumer access to sustainable technology and improvements in the practical useability of sustainable technology.  This factor is often the last remaining gap to cross to ensure frictionless adoption for consumers – if adoption of a technology is falling short of targets despite financial incentives, it is likely that there is still a quality gap, an implied sacrifice consumers are being asked to make in their daily lives to shift to sustainable technology.  For example, one major concern for consumers when considering purchasing an electric vehicle is the charging infrastructure and the distances they may need to travel in between available charge points.  Governments, financial institutions, and investors should consider that investing in EV infrastructure is essential to accelerating the switch from traditional ICE vehicles to electric alternatives.  In Thailand, Kasikornbank has launched WATT’S UP, an EV motorcycle rental and battery swapping platform to promote easier access and increased usage of EV motorcycles.[11]  Several startups throughout the Southeast Asian region are working to enable cross-platform charging and ensure drivers have easy access to multiple charging options in the same way that ICE vehicles are able to refuel at any gas station regardless of vehicle brand.  Alternatively, startups are also tackling green innovations in the public transportation sector, which could make the switch to public transportation more feasible for consumers.  Muvmi, a Thai startup, provides electrified last-mile transport to bridge the gap between established public transit stations and the consumer’s final destination (such as office buildings, popular shopping destinations, or homes).

When looking at ways to decarbonize residential housing, electrification can have a major impact on a consumer’s carbon footprint.  Home electrification projects such as installation of solar panels or electric heat pumps, deployment of EV chargers and battery storage, or weatherization to reduce in-home energy consumption can both decarbonize the home and lead to long-term cost savings.  Startups across the globe, but particularly in developed markets like the US, are leveraging digital technology to solve these kinds of problems.  Companies like Pika and Zero Homes have designed software solutions to enable contractors to simplify the sales process for home electrification projects and improve sales efficiency to speed up adoption by consumers; many startups combine digital tools for sales agents and installers with streamlined payment processes to automate the consumer’s access to financial incentives and minimize the friction in the homeowner’s decision making process.

 

Conclusion

While many discussions on consumer decarbonization may naturally focus on food or consumer goods (which are often more visible issues in the media), a greater degree of impact can be created by focusing solely on decarbonizing home energy usage and consumer transportation.  Past efforts to push decarbonization have also relied on consumers to make the “right” choice, requiring them to pay green premiums or to make sacrifices in their daily lives in order to adopt sustainable technology, which largely results in slow voluntary adoption of said technology.  Consumer mindsets may slowly shift over time, but there is an opportunity in the current market to speed up adoption in the near-term by focusing on reducing the financial barriers and improving accessibility and useability of green solutions.  Governments seeking to hit Paris Agreement targets and financial institutions looking to achieve their net-zero commitments are actively providing financial incentives and new payment models which can reduce the financial burden for consumers to decarbonize.  As discussed previously, financial incentives alone may not be sufficient to trigger mass adoption of green technologies, and therefore many businesses seeking to accelerate their sale of such technologies to the consumer segment look for opportunities, investments, and business models that make the green options easier to use, easier to access, and requiring minimal change from the consumer’s perceived status-quo.  By focusing on these specific areas, ecosystem players seeking to accelerate consumer decarbonization can identify opportunities which can maximize the impact of their investment.

 

Author: Krongkamol deLeon (Joy)

Editors: Benjamas Tusakul (Air), Woraphot Kingkawkantong (Ping)

 

Reference

[1] https://cz.boell.org/en/2023/07/26/individual-carbon-footprint-how-much-does-it-actually-matter

[2] Christopher M Jones and Daniel M Kammen, “Quantifying Carbon Footprint Reduction Opportunities for U.S. Households and Communities,” Environmental Science & Technology, Vol 45/Issue 9,  https://pubs.acs.org/doi/10.1021/es102221h

[3] https://link.springer.com/article/10.1007/s10018-019-00253-7?fromPaywallRec=false

[4] https://www.rabobank.com/knowledge/d011429876-the-rise-of-electric-vehicles-in-the-us-and-the-road-ahead

[5] https://www.rewiringamerica.org/research/pace-of-progress-home-electrification-transition

[6] https://www.bain.com/insights/ten-takeaways-from-our-2024-sustainability-survey-of-consumers-infographic-ceo-sustainability-guide-2024/

[7] https://www.energy.gov/eere/solar/homeowners-guide-federal-tax-credit-solar-photovoltaics

[8] https://www.bangkokpost.com/business/motoring/2871632/cabinet-allots-b7-12bn-for-ev-subsidies#:~:text=Under%20the%20EV%203.5%20scheme,to%2050%2C000%20baht%20per%20vehicle.

[9] https://www.ryt9.com/s/iq03/3479762

[10] https://powerledger.io/platform-features/xgrid/

[11] https://www.kasikornbank.com/en/news/pages/wattsup.aspx

AI and Human Collaboration: A Stronger Cybersecurity Defense

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Cybersecurity refers to the practice of protecting computer systems, networks, and data from unauthorized access, theft, damage, disruption, or other forms of attack. The practice in cybersecurity can involve several technologies, and Artificial Intelligence (AI) is one of the key enabling technologies among many.

This article will explore the importance and use cases of AI in cybersecurity, which bring about both benefits and risks, some risk mitigation methods that are currently being discussed, including how to balance human involvement with AI operations in cybersecurity, implications of AI in cybersecurity for financial service industry, and future trends of AI development in the cybersecurity landscape. This article, inspired by the author’s interest in exploring the role of AI in cybersecurity and finding the right balance with human involvement, aims to share insights from research, the author’s thought processes, and key findings from the exploration.

What is the Role of AI in Cybersecurity? Why is it Becoming Increasingly Important Now?

AI can be categorized into predictive AI and generative AI based on functions they perform. Predictive AI involves models trained on large datasets to identify patterns, correlations, and trends, often used for tasks such as forecasting, classification, and risk assessment. Generative AI, on the other hand, focuses more on generating new content such as text, audio, video, etc., and understand unstructured data.

Predictive AI has been used in cybersecurity since the late 1980s to detect abnormal activities, recognized for its speed and accuracy beyond human capability. However, recent advances in generative AI and machine learning (ML) have contributed both positively and negatively to the cybersecurity realm. To cyber criminals, generative AI, together with ML, allows them to launch more sophisticated attacks at a larger scale such as crafting more realistic phishing emails or creating malware with the ability to adapt its own code to avoid detection by traditional security system. On the other hand, generative AI can be used by cybersecurity team as a countermeasure to enhance threat intelligence by automating learning of unstructured threat data and build a new capability to analyze more qualitative data to improve threat detection.

AI’s capabilities in cybersecurity benefit all organizations, but for large corporations with ample resources, AI improves efficiency and speeds up responses to threats. For SMBs with limited budgets, the benefits are more accentuated in terms of cost reduction for hiring cybersecurity personnel.

With its promising capabilities, organizations are increasingly adopting predictive AI, generative AI, and ML for their cybersecurity practices to boost efficiency and reduce costs by automating labor-intensive tasks. This is, in one way, evidenced through a survey of 800+ senior management by Arctic Wolf where 98% of respondents plan to allocate some portion of their upcoming cybersecurity budget towards AI. Within those, 52% are dedicating over a quarter of their budget in this area.

Note: The term AI used in this article onward means both predictive AI and generative AI together with the capabilities of ML.

AI Capabilities and its Implications to Cybersecurity

AI benefits and capabilities for cybersecurity can be explained through 6 core functions of cybersecurity, which will be further elaborated in the next paragraph. While the benefits offered are not negligible, there are also some restrictions and risks associated with adoption of AI for cybersecurity practices. Organizations must be prepared to mitigate such risks to optimize AI performance.

Cybersecurity can be divided into 6 functions according to the NIST (National Institute of Standards and Technology) framework, a world-renowned framework adopted by global organizations such as Saudi Aramco, Israel National Cyber Directorate, University of Chicago, etc. The definition of each according to NIST is as shown below.

Figure 1: NIST Cybersecurity Framework

  1. GOVERN addresses an understanding of organizational context; the establishment of cybersecurity strategy and cybersecurity supply chain risk management; roles, responsibilities, and authorities; policy; and the oversight of cybersecurity strategy.
  2. IDENTIFY understands the organization in deeper detail (e.g. data, hardware, software, systems, facilities, services, people) and related cybersecurity risks, which enables an organization to prioritize its efforts consistent with its strategy and direction as identified under GOVERN.
  3. PROTECT supports the ability to secure organizational assets to prevent or lower the likelihood and impact of adverse cybersecurity events, as well as to increase the likelihood and impact of taking advantage of opportunities.
  4. DETECT enables the timely discovery and analysis of anomalies, indicators of compromise, and other potentially adverse events that may indicate that cybersecurity attacks and incidents are occurring.
  5. RESPOND supports the ability to contain the effects of cybersecurity incidents.
  6. RECOVER supports the timely restoration of normal operations to reduce the effects of cybersecurity incidents and enable appropriate communication during recovery efforts.

For more information or examples of activities under each function, please visit NIST website.

To understand which tasks are suitable for AI in each function, it is essential to first analyze the expected outcome of functions, which can be done by understanding the objectives. Then, the key success factors can be determined based on qualities that will lead to better outcome of the function, which in turn will be mapped with AI’s unique capabilities to derive suitable tasks under each function. Please see below an analysis of the expected outcome and key success factors for the 6 functions, together with AI/ML capabilities.

Figure 2: Analysis of AI/ML capabilities for Cybersecurity Core functions

Given the unique strengths of AI, particularly its capability to handle vast amount of data, automate routine tasks, and perform real-time actions, AI can help enhance an organization’s cybersecurity posture through all 6 activities, albeit at different capacity.

Even with all the promising capabilities that AI provide, it is a double-edged sword. There are still some downsides that users need to be aware of. There are two major concerns commonly mentioned and discussed across sources.

  1. Ethical concerns:
    • Bias and discrimination in decision-making: This can stem from non-diverse training data set or bias from machine learning process of non-relevant input factors such as gender, race, etc. In cybersecurity, for example, AI might flag certain groups more frequently, leading to unequal treatment. This is especially concerning when using AI in areas like fraud detection or risk assessments where fairness is crucial.
    • Privacy concern from data used to train AI: Data used to train AI is usually retrieved from production databases which might contain personal sensitive information, leading to concerns about data leakage and personal data rights. Data used to train AI for cybersecurity, specifically, often includes personally identifiable information such as user behaviors and biometric information. Strong security measures must be implemented end-to-end from the origination and transmission of data to the handling of data after its use, to ensure no data leakage from the additional exposure.
    • Lack of transparency and explainability: AI is often regarded as a Black Box system, where users can only see inputs and outputs, but not the processes in between. This lack of explainability can cause trust issues, as cybersecurity teams might not fully comprehend why an AI system flags certain behaviors as malicious or overlooks potential threats. Transparency is key to ensuring that AI’s actions align with the organization’s cybersecurity objectives.
  2. Potential Mistakes from AI:
    • Mistakes happen from a model itself such as:
      • A generative AI hallucination – AI models generate incorrect or misleading results, which are caused by a variety of factors, including insufficient training data, incorrect assumptions, and etc. according to Google Cloud.
      • An overfitting of models – AI algorithm fits too closely or even exactly to its training data, resulting in a model that can’t make accurate predictions or conclusions from any data other than the training data according to IBM.
      • An example of this type of mistake in cybersecurity regime is when an AI model flags a legitimate activity as malicious (false positives) or fails to detect actual threats (false negatives).
    • Mistakes happen from malicious actions such as:
      • Data manipulation – Cyber threat actors manipulate data consumed by AI algorithms. By inserting incorrect information into legitimate but compromised sources, they can “poison” AI systems, causing them to error out or export bad information, according to BlueVoyant. For example, attackers might alter logs or feed deceptive data into AI-driven monitoring systems to avoid detection. When AI gradually recognizes such pattern as normal, attackers can then utilize this attack vector for an actual offense.
      • Model theft – AI model itself is compromised and reversed engineered by attackers to find vulnerabilities of the model. Attackers can then exploit weaknesses discovered to launch undetected attacks.

There are currently two main approaches to mitigate these concerns from organizations’ side which are the use of technological solutions and human involvement and supervision. These two options are generally utilized in combination to tackle the concerns.

  1. Technological solutions are seen more often to solve the following concerns.
    • Privacy concern – This concern can be mitigated by using tools to generate synthetic data or perform data masking. There are multiple players providing these solutions such as betterdata, Hazy, Mostly AI.
    • Lack of transparency and explainability – One way to address this concern is to use AI solutions with clear documentation on decision-making processes which can be audited and customizable as needed.
    • Potential mistakes from both a model itself and malicious actions – There has been discussion about building an AI agent to work for humans. AI agent, according to IBM, is “a system or program that is capable of autonomously performing tasks on behalf of a user or another system by designing its workflow and utilizing available tools”. In this case, AI agent can be specifically trained to validate the output of another AI against expected outcome to prevent mistakes.
  2. Human involvement and supervision
    • Bias and discrimination in decision making – To prevent bias and discrimination in AI, ethicists should be integrated into AI development and deployment teams to ensure that training datasets are unbiased, and outputs are rigorously tested for fairness. Geoffrey Hinton emphasized this approach during a session at Collision 2024. More information on AI ethicists can be found here.
    • Potential mistakes from both a model itself and malicious actions – Humans can serve as validators of AI outputs, identifying and correcting any mistakes made by the AI model. This approach follows the ‘Maker-Checker’ principle, ensuring an additional layer of oversight and accountability.

Balancing Human Involvement in the Age of AI Cyber Defense

It is undeniable that AI has unlocked the level of efficiency that is not previously achievable by humans. On the other hand, a human component in cybersecurity operations is still mandatory to mitigate ethical concerns and reduce risks from AI mistakes. Thus, balancing the usage of both components is key to resilient cybersecurity.

Achieving an optimal balance between humans and AI requires a clear understanding of their respective strengths and limitations. Cybersecurity tasks can be categorized into two key areas: operational capabilities and intelligent capabilities. Operational capabilities ensure tasks are executed effectively and efficiently, while intelligent capabilities ensure tasks are carried out responsibly and aligned with an organization’s goals. This analysis helps determine which cybersecurity activities are best suited for AI and which should remain under human supervision. The table below highlights the strengths and limitations of both AI and humans in these areas, with green cells indicating strengths and red cells indicating limitations.

Human AI
Execution Capabilities
Speed Slower processing of large data Swift large scale data processing
Accuracy Prone to human errors Prone to algorithm errors and data poisoning
Consistency Inconsistent performance subject to human limitations Highly consistent performance with 24/7 availability
Scalability Cannot be scaled effectively Easily scalable to handle multiple tasks
Cost efficiency High costs for salaries, training, benefits and difficult to retain High cost efficiency for repetitive tasks
Intelligent Capabilities
Cognitive abilities – Ability to be creative based on personal background – Ability to be creative based on training data
– Capability to contextualize – Low capability to contextualize
– Judgment based on intuition – Intuition by training
Abilities to learn from unlimited sources High dependency on training data fed by human
Slower learning limited by speed of data digestion Speedy learning enabled by computing resources
Emotional intelligence – Understand human emotions and unspoken words

– Personalized interactions

– Emotions by training
Ethics Prone to personal biases Prone to ethical issues from training data

 

As seen in the table above, human and AI possess different strong traits, suggesting rooms to efficiently divide tasks between the two. While small, the overlapping greens or reds point to possibilities of collaboration. When mapped with the NIST framework for cybersecurity, leading roles for each of the 6 tasks and activities that each can perform to strengthen the security postures are as identified in the following figure, where human is tasked to lead roles that require high level of strategic decision-making and communication, while AI is expected to lead the execution according to the preset policies which are more operational by nature.

Figure 3: Human and AI Collaboration on the Cybersecurity’s Core Functions

AI in Cybersecurity for Financial Service Industry

Financial services are frequently cited as a top target for cyber-attacks, with the industry incurring the second-highest breach costs, averaging nearly $6 million annually, according to Nvidia. Due to the high volume and value of monetary transactions, financial institutions are particularly vulnerable to identity fraud and transaction fraud. These forms of fraud are highlighted because identity theft can give attackers access to accounts allowing them to perform fraudulent transactions, while transaction fraud directly compromises the transfer of funds, making them critical concerns for the sector.

Preventions for these two types of frauds often require analysis of high volume of data and involve routine operations in PROTECT, DETECT, and RESPOND functions, in which AI excels at leading the tasks. Therefore, AI is undeniably an effective and efficient defense tools against these frauds for financial service providers to manage the risks. This section will deep dive into how AI has helped the financial service industry mitigate risks of these two frauds and some solution providers.

Identity fraud is “the crime of using someone’s personal information in order to pretend to be them and to get money or goods in their name” according to the Cambridge Dictionary. Some examples of prominent identity frauds include:

1) Phishing – malicious actor sends a phishing content through channels such as email, text message, to account owners, luring them to provide personal credentials or financial information;

2) Fake website – threat actor creates a fake website, looking like legitimate and trustworthy one, deceiving account owners to input financial information or make false financial transactions; and

3) Data breaches – cybercriminal gains access to account owner credentials and information through unauthorized database access or other forms of records.

Transaction fraud is “any deceptive activity intended to acquire money, goods or services during a financial transaction” according to Datavisor. Transaction fraud typically happens after identity fraud, if not at the same time. Cybercriminals use credentials received from identity fraud to perform financial transactions such as using credit card information for unauthorized purchases, using login credentials to perform money transfer to their own accounts.

Several large banks around the world have integrated AI into their cybersecurity measures to protect their customers and minimize their financial losses and reputation damages. Many have announced their strategies on using to address cybersecurity challenges including Bank of America, JPMorgan Chase, KBank, BNP Paribas, Mitsubishi UFG and more. Some outstanding use cases of AI as countermeasures for these frauds being implemented in the financial service sector are shown below.

Identity Fraud Transaction Fraud
PROTECT Biometric authentication – AI is being utilized to perform biometric authentication through methods such as facial recognition, fingerprint scan, and voice recognition to verify account owners in addition to the traditional methods like OTP.

Document verification – AI is being used to verify the authenticity of documents provided by account owners to ensure that it is not a threat actor with falsified documents claiming someone else’s identity.

Solutions providers include authID, Incode, Datacard.

Biometric authentication – Financial service providers are increasingly implementing biometric verification on transactions with values above certain thresholds to limit the risks of transactions initiated by unauthorized threat actors.

Solution providers are usually the same as those providing authentication and verification solutions for identity fraud.

DETECT Customer profile analytics – AI can collect a customer’s device ID, IP address, geolocation, and behavioral biometric clues such as typing speed, pressure and the angle at which a customer typically holds their phone to create a customer’s profile. Deviations from normal patterns can be flagged as anomalies.

Solution providers include BioCatch, Socure.

Customer behavior analytics – AI can learn customer’s normal patterns of spending including types of expenses, normal ticket sizes, time and place of transactions, and etc. Any abnormal spending behaviors are then flagged for further actions.

Solution providers include SEON, feedzai, Verafin.

RESPOND Real-time alerts – AI can automatically alert customers for potential identity and transaction frauds flagged in the DETECTION or PROTECTION phases and prompt them to change passwords and act through a verified channel to confirm if it is their legitimate action.

Real-time suspension – In a more serious case, AI can even decide to force a logout and suspend an account, and request customers to verify themselves through channels such as phone call before resuming their activities.

The RESPOND features often come with the DETECT features; therefore, solution providers in this case are the same as those providing solutions for DETECT function.

 

Conclusion

AI has become essential in cybersecurity, offering new capabilities for both attackers and defenders. While AI enables faster, more widespread cyberattacks, it also empowers defense mechanisms to counter threats at unprecedented speed and scale. The effectiveness of AI grows with more data, making it a race where “data is the new oil,” as Clive Humby noted. However, AI is a double-edged sword, with ethical concerns and potential errors posing significant risks. To mitigate these, the industry is balancing AI’s strengths, like rapid data analysis and automated responses, with human oversight for tasks requiring context and nuanced judgment. One of the prominent use cases is for the financial service industry, which deals with high volume and value of monetary transactions. AI is being widely adopted to prevent identity fraud and transaction fraud due to its strengths in speedy high volume data analysis and routine task automation.

The AI era is just beginning, with many future possibilities to strengthen cybersecurity. One promising initiative is cross-environment intelligence, where AI models can learn from data across multiple organizations without exposing sensitive information, creating real-time collective intelligence. However, this requires central coordination and standardized integration across systems, making it a work-in-progress. Another development is the rise of AI agents, which can integrate with systems to automatically perform cybersecurity tasks using available tools and applications, and collaborate with each other, like humans, to enhance security and push automation further in cybersecurity operations.

As we venture into this ever-evolving landscape of cyber threats, organizations must stay informed on emerging trends and technologies to remain resilient, with AI being at the forefront. However, the use of AI in cybersecurity will require human supervision to ensure ethical outcomes, prevent mistakes, monitor undocumented data, and make strategic decisions. Only with this balance between AI and human oversight can organizations fully harness the potential of AI to effectively enhance their cybersecurity defenses.

Author: Benjamas Tusakul (Air)

Editors: Wanwares Boonkong (Pin), Woraphot Kingkawkantong (Ping)

Reference

https://www.sophos.com/en-us/cybersecurity-explained/ai-in-cybersecurity

https://www.engati.com/blog/ai-in-cybersecurity

https://talkbusiness.net/2024/03/the-pros-and-cons-of-ai-in-cyber-security/#:~:text=The%20Cons%20of%20AI%20in%20Cyber%20Security&text=AI%20tools%20themselves%20have%20become,Deepfakes%20are%20also%20a%20risk.

https://www.securitymagazine.com/articles/99487-assessing-the-pros-and-cons-of-ai-for-cybersecurity

https://www.statista.com/statistics/1382266/cyber-attacks-worldwide-by-type/

https://www.weforum.org/agenda/2024/01/cybersecurity-cybercrime-system-safety/

https://www.entrepreneur.com/science-technology/how-ai-can-improve-cybersecurity-for-businesses-of-all-sizes/476727#:~:text=Artificial%20intelligence%20plays%20a%20dual%20role%20in%20cybersecurity,growth%20of%20cybercrime%20in%20the%20next%20few%20years

https://www.ey.com/en_gl/insights/consulting/transform-cybersecurity-to-accelerate-value-from-ai

https://arcticwolf.com/resource/aw/the-human-ai-partnership 

https://academia.co.uk/ai-versus-human-collaboration-for-a-secure-digital-future/

https://secureframe.com/blog/ai-in-cybersecurity

https://www.paloaltonetworks.com/cyberpedia/generative-ai-in-cybersecurity

https://outshift.cisco.com/blog/adopting-ai-security-operations

https://www.techmagic.co/blog/ai-in-cybersecurity/

https://www.americanbanker.com/news/can-ai-help-when-a-scam-is-invisible-to-the-bank

https://innov8tif.com/6-ways-ai-is-fighting-back-against/

https://www.cio.com/article/190888/5-famous-analytics-and-ai-disasters.html

https://www.splunk.com/en_us/form/state-of-security.html

https://www.tableau.com/data-insights/ai/advantages-disadvantages

https://www.theimpulsedigital.com/blog/ai-vs-human-intelligence-exploring-the-advantages-and-limitations/

https://www.radware.com/blog/security/threat-intelligence/2024/06/beyond-chatgpt-how-ai-agents-are-shaping-the-future-of-cyber-defense-and-offense/

Innovation Trend to Watch in 2025 & Beacon VC 2024 Interim Update

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The year 2024 has been another eventful year at Beacon VC. This year we have channeled our energy to continue being a vibrant member of the Thai and Southeast-Asian startup community. With several programs aiming to enhance business capability of Thai startups ranging from offline training course on ESG to mentorship program, Beacon VC was recognized by Techsauce as the Startup Backbone. On top of that, we have relentlessly continued to build our  understanding in several emerging technological themes, from cybersecurity, compliance tech, ESG economy infrastructure, to revisited some existing themes such as Blockchain and AI.

With nine months passed into the year, Beacon VC wishes to update our readers on the prominent innovation themes that has taken hold in 2024, and will shape the innovation community, corporate interest, and regulator conversion in 2025.

Underlying forces shaping innovation trend

Continued economic uncertainties and geo-political tensions forces business to rethink cost per growth

The global economic growth is evidently slowing down in 2024, as a result of higher interest rates and geopolitical uncertainties weighing on consumption and investment. The global trade arena has been increasingly difficult to navigate with the heating up of the geo-political scene, affecting anything from supply chain continuity to fluctuation of major currencies. The growth gap between advanced and developing economies is expected to widen. Both major economies such as the US, the Eurozone, and China, and emerging economies like ASEAN are all expected to face distinct challenges that will keep growth subdued from economic bubbles, internal political unrests, to plaquing societal problems. The global economic prospect for 2024 will likely see a slowdown in GDP growth, with expectations set at 2.7%. 

During this time of turbulence, businesses still face pressure from investors to grow, but not at all costs. Companies, conventional or startups alike, are finding ways to maximize marginal revenue at minimum marginal costs, and hence have turned to automation, infrastructure-as-a-service providers to reduce CAPEX, and ecosystem-wide partnership efforts to share fixed costs.

AI made its way to boardroom discussion of companies and regulators, but the execution is slow-coming

The rapid advancements in AI capabilities, such as natural language processing and computer vision, will further fuel its integration into various industries. The market size is expected to show an annual growth rate (CAGR 2024-2030) of 15.83%, resulting in a market volume of US$738.80bn by 2030. With a tremendous development in Generative AI, this segment is expected to expand at CAGR of 42% over the next 10 years driven by training infrastructure in the near-term and gradually shifting to inference devices for large language models (LLMs), digital ads, specialized software and services in the medium to long term.

The proliferation of AI use cases have caught the interest of both companies, who are finding ways to integrate the solution into current workflow to bolster efficiency or unlock human resource creative potentials, and regulators, who are worrying about misuse of AI or snowballed AI biases. On one hand, the industry is witnessing an increase in corporate POCs with enterprise AI solutions, but only those proceeding to real adoption demonstrate real monetary benefit, deriving from the pressure to uphold profitability and proposition that’s relevant to company’s unique macro and micro setting (for example, Thai companies may be more interested to reduce human error than reducing payroll cost savings). On the other hand, regulators, acknowledging the intricacy of the subject and technology’s nascent development, have issued guidelines and recommendations as opposed to stringent detailed rules. An example of AI recommendation for Thai executives developed by AIGC under ETDA can be found here.

ESG consideration entering mainstream mentality and ramping up growth for ESG startups

The use of data by businesses and investors to assess and manage their environmental, social, and governance (ESG) performance has become increasingly common over the last few years. This trend has continued in 2024, with a focus on data-driven ESG initiatives across a variety of industries. Several major regulators and governments passed climate bills and reporting requirements into law, setting up 2025 to be an important year for ESG and corporate sustainability. New technologies like blockchain and artificial intelligence are improving data transparency and enhancing ESG trends analysis, boosting the accuracy and efficiency of reporting, but also are being criticized for their consumption of energy from carbon-intensive sources and water.

The rush of ESG adoption and system overhaul in organizations alongside the realization of internal implementation capability spark upsurge in corporates looking to adopt startup solutions for this endeavor, ranging from waste management, smart water treatment, carbon footprint calculation, energy consumption optimization, to internal information mapping.

Enhanced data capability through IOT, 5G, and AI synergy

5G is the fifth generation of cellular networks with up to 100 times faster than 4G, 5G is creating never-before-seen opportunities for people and businesses. It is expected to grow 10-fold by 2030 with network expansion in several markets. Faster connectivity speeds, ultra-low latency and greater bandwidth is advancing societies, transforming industries and dramatically enhancing day-to-day experiences.

This enhanced connectivity also comes with a colossal amount of data being generated and, with the help of AI, these large data sets can be analyzed real time to inform people and businesses of the decision to be made based on specific circumstances. Nevertheless, there’s still on-going discussion on the governance framework and data management techniques to ensure that these data are truly in-compliant with data privacy principles

Innovation Trend to Watch in 2025

Marketing Reengineered and Customer Value Reimagined

  • Emergence of new Value Propositions: As consumer sophistication increases and needs become evermore fragmented, the definition of value has transcended beyond ordinary price-tag into experiences, alignment with value, durability, or fit with personal liquidity. ESG value is amongst the latest value propositions that consumers are starting to show willingness to pay for. This new definition of value requires businesses to rethink strategy and key activities for customer acquisition and CRM.
  • Hyper-Personalization: Increased real-time data points and availability of enterprise-grade AI unlock opportunities for micro-targeting, from segment based marketing efforts down to individual level, allowing for more personalized offerings and experiences, more seamless experience online to offline, and more effective value proposition design aligned with evolving customer needs.

Harvesting the Power of Data

  • Decision-making and Analysis Made Simple: Data analytics including the use of AI can extract profound insights from data, guiding strategic decision-making, product development, and marketing campaigns. For example, there is a proliferation of AI-assisted software that help research labs discover new chemical or material formulations, that would both enhance the effectiveness of the research process, reduce cost to new product discovery, and speed up the time to market.
  • Optimization and Automation Revolution: AI can automate data collection and processing, making it faster, more reliable, and cost-efficient. This frees up human resources for higher-level tasks and lets start-ups capitalize on data insights more effectively. Formerly labor-intensive tasks such as invoice matching or data entry are being replaced by AI helpers, and the role of humans transcend from executor to quality checker.
  • Uncover New Business Opportunities: AI analysis can uncover trends and unmet needs, leading to groundbreaking solutions and business opportunities. Such opportunities include new revenue generation potentials based on consumer psychographic segmentation and behavior, cost reduction along the supply chain, or identifying weak points within the business operation.

Digitizing Legacy Pillar of Business – Trust & Compliance

  • Rush for Cybersecurity Fortress: As anxieties rise and connectivity brings increased vulnerability, data privacy and security become non-negotiables. Startups are in the rush to offer solutions that would help clients preserve security and anonymity of information and transaction, while not compromising on user experience.
  • Simplifying Compliance Activities: The tightening regulatory landscape around ESG and PDPA/GDPR necessitates active compliance to avoid legal consequences. Among the early challenges that corporates and startups need to overcome is an efficient process to map and manage data, consent, and relevant stakeholders. On top of that, there’s an increasing demand for solutions that would help manage reporting activities for internal control and external regulators.
  • Responsible Use of Technology: As AI becomes more prevalent, concerns about bias and fairness will intensify. Companies that embrace responsible AI development, ethical decision-making frameworks, and transparent data-driven algorithms will build trust and mitigate potential societal risks. The effort to de-bias AI requires philosophical, technological, and data analytic capability that most companies, albeit large or small, have access to. The industry can expect to see the emergence of third party solutions to conduct bias/ algorithm audits, or debiasing tools for model training.

Cultivating Human-AI Synergy

  • Augmenting Human Capabilities: AI will not replace humans, but rather augment their skills. Humans will focus on creative tasks, strategic decision-making, and ethical oversight, while AI handles data analysis, repetitive tasks, and optimization. The industry will witness more use cases of AI-copilots moving from POCs to real adoption. Early use cases of such transition may include front-end functions relating to sales outreach or customer handling, and back-end functions such as data cleaning and entry and resource planning.
  • Creating Impacts through Collaboration: AI becomes a powerful tool for addressing social challenges, managing resources sustainably, and promoting environmental responsibility. However, as much as AI can add value to humankind, human expertise is critical to guide AI development and ensures ethical considerations are upheld. The industry can expect continued discussion on AI development guidelines, but the conversation will expand beyond the circle of technology industry leaders and regulators, to be joined by philosophers, human right activists, and academia.

Beacon VC Investment Activities in 2024

Since the past update, Beacon VC has gotten approval for seven new investments. These investments operate from various geographical regions, from Southeast Asia, Europe, and North America, and cover many verticals from HRM, enterprise blockchain, carbon footprint assessment, to enterprise efficiency tools.

Opportunistic Fund

  • [Direct Investment] (Official) HumanSoft – Thai HR solution platform for SMEs and corporates: HumanSoft is a customizable cloud-based HR solution designed for SMEs and corporates, streamlining HR tasks to enable business owners to focus on their core operations. The platform supports a wide range of complex HR activities, including shift management, various clock-in/clock-out methods, payroll calculation, employee onboarding, and development.
  • [Direct Investment] Singaporean Conversational AI solutions: Generative AI is becoming a critical part of businesses especially for customer support. This company helps enterprises efficiently engage customers with its human-like AI solutions with ranges of activities from loan collection, sales outreach, to complex customer service.
  • [Direct Investment] Thai Customer Data Platform: This Thai-based startup provides a solution to better collect, store and manage customer data. It leverages technology to track unknown to known data supporting corporates to better store and utilize data in a structured manner.
  • [Fund Investment] Southeast Asia-focused Blockchain Infrastructure Fund: The fund seeks to support the shift towards a decentralized digital economy where liquidity and user participation are seamlessly integrated.  It focuses on investments in regulated digital asset platforms or solutions, decentralized finance (DeFi), infrastructure, and consumer applications.

Impact Fund

  • [Direct Investment] A British SaaS platform for calculating carbon emissions: The platform allows companies to see which processes are responsible for emissions, and identifies specific recommendations for companies to reduce emission by leveraging scientific research. The company aims to help multinational companies reduce scope 3 emission in their supply chain.
  • [Direct Investment] An American SaaS platform for product life cycle assessment: The platform helps FMCG and retailers track the carbon footprint of the entire product lifecycle, starting from the upstream in sourcing and manufacturing process to downstream in disposal and recycling process. This gives insight on how to optimize the footprint and tools for brand owners to fully comply with growing regulator demands.
  • [Direct Investment] An Indonesian comprehensive micro-SME business solution. The company aims to equip micro-SME businesses in Indonesia with all-in-one business solutions ranging from accounting, HRM, sales and inventory management, as well as financial management, with hopes that these businesses will thrive in the Indonesian market and become financially included in the formal financing sector.

Published researches in 2024

This year Beacon VC moved our pursuit of self-education from focusing wholly in ESG space to more emerging topics within the innovation community.  Thus far, Beacon VC published four articles with hopes to be a profound thought-starter for our readers: Unlocking the Power of Reals: How Blockchain is Revolutionizing the Future of Finance, Conversational Banking and Why It Matters, Potential ESG Impacts in Startups Created by Early-stage Minority Investors, and How Socioeconomic Status Affects Thai Education Inequity and How Stakeholders in the Community Can Address It. Stay tuned for more articles that would ride along the innovation theme we have described earlier.


 

Unlocking the Power of the Reals: How Blockchain is Revolutionizing the Future of Finance

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Image generated by Gemini

Blockchain technology has come a long way since its emergence with Bitcoin (Blockchain 1.0) and the introduction of Ethereum and smart contracts. Core promises including increased decentralization, transparency, traceability, efficiency, and automation have fueled its development. Today, there’s a tremendous push to build the infrastructure of the future, with a vision to disrupt and create a better financial landscape. With growing clarity on blockchain’s potential and evolving regulations, the industry sets its sight on mass adoption. Today, the focus on “real-world use cases” becomes topics of conversation. These use cases span from cross-border payments and asset tokenization for democratized investment to identity verification.

However, a critical first step is taking a step back and addressing fundamental questions: Why focus on the Reals (Real Money, Real Assets, Real Identity)? What problems are we solving? Why is now the time?

This article dives into defining the Reals, answering these crucial questions related to real-world use cases. We will also explore the why and how Financial Institutions (FIs) can participate in driving mainstream adoption and identify the gaps they need to address to pave the way for the future of finance.

Overview of the “Reals”

Before diving into the core questions, let’s establish a clear definition and framework of “the Reals”.

For this article, we will utilize the definitions established by Quarix, a blockchain infrastructure developed by Orbix Technology under Kasikornbank. Their framework resonates well with our focus, offering comprehensive definitions in the financial context while emphasizing the crucial element of “real” – a secure and verifiable representation within the digital realm.

The following is the breakdown of the “Reals”:

  • Real Money: Tangible money based on national currency that can be used for all practical purposes
  • Real Assets: Tokenization of real-world assets such as bonds, capital market assets etc. on the blockchain
  • Real Identity: Secure on-chain verification of users to ensure every user’s authenticity and identity

By focusing on these Reals, we can explore how blockchain technology is revolutionizing the way we manage and interact with money, assets, and identities in the digital age.

Why ‘Reals’ Matter

The widespread adoption of blockchain technology hinges on its ability to tackle real-world problems, particularly within the financial sector that has numerous inefficiencies and vulnerabilities and demand better and more streamlined products and services. Blockchain’s core strengths – decentralization, immutability, and traceability – offer immense potential for streamlining operations across various industries. However, focusing on applications anchored in the Reals framework (Real Money, Real Assets, and Real Identity) unlocks its true potential for financial services.

There are three main fundamental questions that we need to answer related to the development of real-world use cases leveraging blockchain technology. Let’s deep dive into each of the questions.

1. Why Do We Need to Focus on the “Reals”?

The financial landscape is ripe for disruption. The “Reals” framework can address fundamental challenges hindering the current financial landscape and unlock the true potential of blockchain technology. Here’s why focusing on Reals is crucial:

    • Grounding Innovation in Reality: The “real” in Reals emphasizes that these applications are grounded in verifiable reality, not purely speculative ventures. This focus fosters trust and confidence in blockchain’s ability to revolutionize financial services.
    • Solving Existing Problems: Traditional systems managing money, assets, and identities are often plagued by inefficiencies and security vulnerabilities. Blockchain’s cryptographic technology safeguards data integrity, making it incredibly difficult to hack or manipulate information. Additionally, blockchain technology has the potential to build new business models that could generate new revenue streams and optimize costs.
    • Achieving Mainstream Adoption: By focusing on the Reals, blockchain addresses issues directly relevant to everyday users and businesses. This focus on tangible benefits creates a compelling case for adoption and drives user confidence in the technology.

Additionally, the “Reals” framework offers a transformative approach to blockchain, fostering trust and revolutionizing finance for all stakeholders. It empowers the blockchain community to drive mass adoption by focusing on real-world problems and fostering user trust. For financial institutions, Reals unlock a competitive edge with innovative products and services and cost optimization, while enhancing inclusivity. Regulators benefit from a more robust and efficient financial system through transparency, traceability, and secure identity management. By prioritizing tangible applications grounded in reality, Reals pave the way for a trusted future of finance.

2. What Problems Are We Trying to Solve?

While the possibility for fantastical application exists, grounding blockchain in the “real-world” in the financial sector addresses several key issues:

    • Inefficiencies within Traditional Finance Realm: Traditional systems for managing money, assets, and identities can be complex and slow, leading to inefficiencies and errors. Blockchain offers automation capabilities such as document verification and processing that can streamline the tracking of goods and documents. As automation occurs across a distributed network, it eliminates a single points of failure and ensure transparency in every step. For instance, a World Bank report highlights that trade finance inefficiencies cost an estimated $1.5 trillion globally each year. By streamlining trade finance processes with tokenized assets, the technology can help unleash the potential for cost savings.
    • Vulnerable Security: The digital world is rife with opportunities for manipulation, fraud, and data breaches that expose sensitive financial information and user identities. Traditional systems are vulnerable to these threats. Blockchain technology offers a solution: its tamper-proof architecture creates a verifiable audit trail, significantly reducing fraud risks. Additionally, blockchain’s cryptographic protocols make it incredibly difficult to hack. Data from Chainalysis, reported by The Record, highlights this issue – DeFi platforms alone saw $1.1 billion stolen in 2023, with incidents rising from 219 in 2022 to 231. By focusing on the Reals, blockchain can create a more secure ecosystem for financial transactions and identity management.
    • Lack of Transparency: One of blockchain’s core strengths is transparency. All transactions are recorded on a shared ledger, accessible to authorized participants. This shared ledger acts as a single source of truth, providing real-time visibility into the status of transactions and movement of goods for all parties involved. Walmart Canada exemplifies this by leveraging blockchain technology to tackle supple chain challenges and develop a solution for invoices management. With clear audit trail that blockchain technology enables, it fosters another level of trust in the new financial system. Unlike traditional opaque systems, everyone involved can track the progress and verify the legitimacy of transactions.

3. Why Do We Need to Focus on It Now?

The urgency to address the real-world problems with blockchain is intensifying mainly due to several converging factors:

    • Unlocking Sustainable Growth: Financial activities are rapidly moving online driven by higher efficiency and better convenience. This increasing reliance on digital systems emphasizes the critical need for secure and trustworthy infrastructure. With its immutability and transparency, blockchain technology is well-positioned to address the need, build trust and enabling sustainable growth in the new financial landscape.    
    • Meeting Evolving Customer Demand: In a digital world demanding ever-better financial solutions, businesses and consumers prioritize efficiency, security, and cost-savings. Traditional systems often struggle to meet these expectations. Blockchain technology offers a compelling solution, delivering substantial improvements at minimal cost. Customers are unconcerned with the technology itself, but rather the improved product or service. Blockchain excels in this matter, streamlining back-end processes with smart contracts, enhancing security through cryptography, and potentially reducing costs for all stakeholders by eliminating intermediaries.
    • Securing a Competitive Edge: In today’s competitive landscape, the key to differentiation lies in ecosystem integration. Blockchain technology, as a shared infrastructure, allows businesses to build all-inclusive ecosystems where partners can collaborate freely. This fosters innovation and value creation across the network, creating a powerful “lock-in effect.” Early adopters who leverage blockchain’s potential to build robust ecosystem become pioneers, attracting partners and establishing a long-term competitive edge.

Financial Institutions and the “Reals” Revolution

Having explored the critical questions surrounding blockchain technology and the Reals, let’s dive into why financial institutions should participate in this evolving financial landscape. If they choose to embrace this revolution, how can these incumbents actively be involved? Furthermore, what are the key gaps they need to address to fully capitalize on the ‘Reals’ revolution?

1. The Rationales Why the Reals is a Strategic Imperative for FIs

The financial landscape is rapidly evolving, driven by innovative technologies like blockchain. To stay ahead of the curve and solidify their leadership positions, FIs need to embrace the “Reals” framework: Real Money, Real Assets, and Real Identity for the following reasons:

    • Building a Competitive Future with Existing Strengths:
      • Innovative Products and Enhanced Security: Blockchain technology allows FIs to leverage their existing expertise in security and data management. By focusing on Reals, FIs can develop innovative financial products with a foundation of trust and security. For example, HSBC’s pilot project in the UAE used blockchain technology in the know-your-customer (KYC) process. This initiative allows the secure sharing of verified KYC data between banks and licensing authorities in the UAE, which could simplify the onboarding process for the clients while maintaining data integrity. This focus on security fosters client adoption and positions FIs as trusted leaders in the evolving financial landscape.
      • Cost Optimization and New Revenue Streams: Reals-based solutions can unlock significant cost savings and create entirely new revenue streams. Streamlining processes and reducing transaction and settlement costs with blockchain translates to a more competitive edge, as seen with Franklin Templeton’s tokenized money market fund. With this initiative, tokenization opens doors to new investment opportunities for both existing and new clients, secondary trading possibilities and collateral use, generating additional revenue sources.
    • Securing Future Share of Wallet:
      • Foundation for Seamless Integration: Building infrastructure that aligns with Reals allows for more effortless integration with future Web3 and blockchain applications. This establishes FIs as key players in the evolving ecosystem.
      • Network Effect: Early adoption of Reals helps FIs cultivate a robust network of participants within their ecosystem. As new applications and services emerge, this network effect positions them for continued growth and relevance.

2. How can FIs Dive into Real

There are growing real-world use cases that leverage blockchain technology. Instead of listing potential use cases that FIs could explore in Real Money, Real Assets, and Real Identity, this article will discuss the framework we deem essential for FIs to consider. This framework will guide them in exploring and prioritizing which Reals use cases they should develop and launch.

Prioritizing The Reals Use Cases

To navigate the exciting possibilities of the Reals, FIs should consider and achieve these three aspects:

    • Problem-Solving Impact:
      • Focusing on Real Problems: Identify pain points in existing money, asset, and identity management from within the organization and the clients. This could be slow cross-border payments, opaque trade finance, or cumbersome KYC processes
      • Aligning with Business Goals and Client Needs: How can a solution address strategic goals like cost reduction, revenue growth, or improved customer experience? Solutions built with Reals should leverage familiar and intuitive UX/UI, ensuring a smooth transition and minimizing the learning curve for the users
      • Assessing the Impact: Assess potential impacts in terms of the implementation in both financial and technical aspects
    • Technical Viability:
      • Leveraging Existing Solutions: Can existing platforms or solutions be adapted to address the identified problem?
      • Evaluating Client’s Resources: Consider client’s capability and willingness to adopt and implement blockchain technology
    • Regulatory Compliance:
      • Compliance Considerations: Evaluate potential regulatory hurdles related to privacy, customer data protection, cybersecurity etc.
      • Risk Management: Balance the innovation with customer protection and financial stability

Balancing the Framework: Why All Three Factors Are Important

The framework we presented emphasizes the importance of considering all three factors – Problem-Solving Impact, Technical Viability, and Regulatory Compliance – to ensure successful Reals implementation. Here’s why each factor plays a crucial role:

    • Problem-Solving Impact + Technical Viability ≠ Success: Even if a use case addresses a real problem and is technically feasible, it could be an ‘illegal innovation’ if it clashes with the regulations.
    • Problem-Solving Impact + Regulatory Compliance ≠ Reality: A focus solely on solving a problem and adhering to regulations could lead to unrealistic solutions, like us in the ‘dreamland’.
    • Technical Viability + Regulatory Compliance ≠ Innovation: Focusing solely on the technology and regulations could lead to ‘overengineering’ solutions that do not address the core problems effectively. Sometimes, existing solutions with minor adjustments can achieve the desired outcome.

By taking all three factors into account, FIs should be prioritizing Reals use cases that deliver tangible and impactful benefits to the organization and/or clients, technically achievable while complying with current regulations to avoid potential roadblocks.

3. Bridging the Gaps

Despite the promise, there are challenges for FIs to address for a successful implementation of use cases in the Reals:

    • Regulatory Uncertainty: A major hurdle is the evolving regulatory landscape surrounding blockchain. The concerns can evolve around market stability, ownership of on-chain assets, investor protection, data privacy etc. FIs need a guideline from the relevant regulator(s) to move forward with confidence.
    • Technical Hurdles: The main technical issues are integration with legacy system, scalability limitations and standardization issue. Integrating blockchain solutions with existing core banking systems can be complex and expensive. In terms of scalability, FIs need to explore protocols that can simulate and handle real-world financial application. Due to differences in development standard, it is crucial to have a secure interoperability solution in place and/or develop a common standard for data formats and communication protocols.
    • Building Trust and User Adoption: Public education and intuitive user experience are the key to accelerate a mainstream adoption of blockchain and the use cases in the Reals. FIs need to invest in educating consumers about the benefits and security features of blockchain-based solutions, while developing user-friendly solutions to interact with decentralized applications.
    • Talent Acquisition and Skills Gap: Specialized skills such as blockchain architecture, smart contracts, data structures are required to launch and operate blockchain-based products/services. FIs need to invest in talent acquisition programs to attract developers and other business professionals, while providing training programs on blockchain basics to existing employees to narrow the skill gap.

Closing Thoughts: A Future Empowered by the Reals

With a clear understanding of the importance of Real Money, Real Assets and Real Identities powered by blockchain, the technology offers exciting possibilities and transformative path for FIs. By prioritizing Problem-Solving impact, Technical Viability, and Regulatory Compliance, FIs can create impactful results for all stakeholders. Consumers gain control and security, businesses find efficiency and innovation, and the economy thrives on inclusion and transparency. Overcoming the mentioned challenges can unlock immense potential, paving the way for a more secure, efficient, and innovative financial future.

 

Author: Wanwares Boonkong (Pin)

Editor: Panuchanad Phunkitjakran (Pook), Woraphot Kingkawkantong (Ping)

 

Reference:

Conversational Banking & Why It Matters Now.

Posted on by [email protected]

Image generated by Gemini

Given the rapid changes in customer behavior, incumbent financial institutions must adapt to remain competitive in the market. Customer touchpoints and experiences become increasingly important factors in differentiating their position.

As digital banking has become increasingly common, customer expectations for timely support have understandably risen. However, financial institutions often struggle to achieve deep consumer engagement solely through mobile apps. For more complex products like investment funds, bonds, or intricate investment vehicles, human assistance remains crucial. Human advisors can guide customers, understand their risk appetite, and find suitable wealth products. Some customer requests cannot be done purely by clicking the button. It is important for financial institutions to understand the “situation” and “unique requirement” customers are demanding, while being attuned to the customer emotional state, and response accordingly. Since customers are active on various channels, effective communication and engagement method are vital in capturing their attention and meeting their expectations.

Therefore, banks must consider leveraging new technology and automation, on top of already existing tools, to boost customer satisfaction while streamlining operational costs. One promising solution is “conversational banking.”

Conversational Banking: An Evolution of AI for Financial Services

Conversational banking is a rapidly growing trend in the financial services industry, improving the way banks interact with customers by leveraging technologies such as artificial intelligence (AI), in conjunction with existing chatbot, to deliver more personalized and accessible banking experiences.

Customers nowadays often expect faster response time from their preferred communication channels. Chatbots are among the most common applications to expand banks’ service time to 24/7 and the earliest applications of conversational AI in banking. They are on the edge to help customers from mundane financial activities such as transferring money and checking account balances, to less straight-forward activities such as account opening, managing investment portfolio, and negotiating credit card payment terms, all without the need to tediously scan through websites or apps or wait on hold for a call center.

However, due to their constraints around the ability to comprehend only specific use cases and precise keywords, chatbots often force customers to constantly adapt their language to structured commands or predefined phrases, which can be frustrating for customers and loss of opportunity for banks. Based on this pain point, conversational banking offers a more natural ways for customers to interact with banks, and the engine adapts over time using machine learning to learn from past interactions and make the chatbots smarter.

In short, conversational banking takes traditional chatbots to the next level. It empowers chatbots to learn from past interactions and anticipate user needs, while able to understand natural language used by customers, resulting in a more engaging experience through “human-like” interactions. These “smarter chatbots” leverage various technological tools to enhance their capabilities and achieve more for both customers and banks.

The Tech: Automation of Human-like Interactions from Rich Data

Unlike static chatbots, conversational AI learns from every interaction with its users. Each conversation feeds its machine learning (ML) engine, enabling it to handle advanced terms, local slang, and dialects. Behind conversational AI, there are various supporting technological tools to make the bot learn, analyze and response.

In a non-technical term, the AI first tries to understand what a user is asking, then chooses the best way to respond, and finally makes sure it sounds natural. If the user is using voice, it also needs to understand what the user says, both message and tone, and replies clearly and empathetically.

As illustrated above, conversational banking leverages a range of technologies to resolve customer queries including Natural Language Understanding (NLU), Dialog Management, Natural Language Generation (NLG), and Automatic Speed Recognition (ASR) (details of each tech are noted in the table below). Each tool works together to make the bot function. NLU and NLG are parts of Natural Language Processing (NLP).

NLP offers a range of powerful capabilities, including speech recognition, speech-to-text conversion, and text-to-speech synthesis. Furthermore, NLP can now even identify emotions in text or speech, adding a new level of understanding to human-computer interaction.

Importantly, the machine learning tool helps the bot analyze and learn from past conversations, and applies them to the conversation at hand.

Table 1: Bundled AI tools help the bot work and learn.

Functions Tools
Understand the intend behind a text Natural Language Understanding (NLU), a part of NLP
Form a response Dialog Management
Generates a response in a human-friendly manner Natural Language Generation (NLG), also a part of NLP
Convert speech to text and text to speech Automatic Speed Recognition (ASR)
Learn from experience Machine Learning (ML)

Due to technology like NLP and ML, AI has become smarter and more human-like.  Additionally, another key development driving this progress is the data used to train models. In the past, traditional single-model AI relied on a single source or type of data for specific tasks such as scribing texts from the internet to teach AI. However, multimodal AI ingests and processes data from multiple inputs like text, video, images, and speech. By combining relevant data from various sources (not limited to text), AI has vast amounts of information to learn and analyze, which could be put together into smarter and more engaging responses.

The Promise: What Human-Like Chatbots May Bring to the Market

The way financial institutions interact with customers is constantly evolving. Conversational banking disrupts these traditional methods by leveraging new technology to respond to ever-changing customer behavior. For the purpose of this article, we can examine the impact of technological advancement on how banks interact with customers.

Tech Advancement: From Static to Dynamic Scripting

As mentioned earlier on chatbots vs conversational AI, the technology advances from bots responding to simple questions or requests, based on pre-analyzed models and logic, to adding some sorts of analytics and personalization. Today, due to reduced technology costs of AI development and cloud computing, real-time analytics is made possible. Bots could gather data from various sources in real-time to analyze and predict customers’ next requests.

The form of conversational AI is enabling banks to move away from making simple requests like transferring money, to provide deeper analysis such as spending graphs with monthly comparisons, to personalized solutions for each user, and real-time monitoring to report and support. Now the next move is Anticipative Interaction. AI anticipates customers with specific needs before they even reach out. Even before customers get in touch, an AI-supported system can anticipate their likely needs and generate prompts for the agent (as co-pilots) or for decision maker to fabricate pre-made solutions. For example, the system might flag that the customer’s credit-card bill is higher than usual, while also highlighting minimum-balance requirements and suggesting payment-plan options to offer. If the customer calls, the agent can not only address an immediate question, but also offer support that deepens the relationship and potentially avoids an additional call from the customer later.

Interactions: From Reactive to Anticipative

  1. Where or Channels

Customers are migrated from traditional branches to digital channels. Traditionally, the main interaction between banks and customers is between bank branches and ATMs. Today, mobile banking is becoming a main point of contact, therefore, call centers and chatbots are playing larger roles in customer support.

Soon, chatbots and conversational AI will expand to every touchpoint of clients. The service could be integrated with different apps (rather than mobile banking apps) and channels (even branches to lower headcount costs) that users are interacting with. One example is the accessibility through third-party messaging services and social media platforms to improve the experience to customers. A customer may see a news recap on political conflict in a neighboring country on social media, forward it to the bank’s official account chat-box, and inquire about next steps on mitigating the effect on his/her investment portfolio. Customers would benefit from 24/7 support, reduced waiting time, and more personalized responses.

  1. What or Use cases

In the past, banks primarily used conversational AI for customer verification (Know Your Customer or KYC) and handling simple transactions like balance checks and money transfers. However, as advanced conversational AI can learn from experience, it opens doors for new use cases in the financial service industry.

The use of data can supercharge the bots’ analysis, providing deeper customer insights and fostering broader financial service integration. For example, conversational banking could support pre-qualification for loans by analyzing non-financial data such as voice to predict credit scores. It could also remind customers about upcoming payments based on their spending patterns or approve loan extensions using past repayment and social media data.

Looking to the future, conversational banking is expected to extend beyond banking products and services. By connecting data with third-party platforms, it could help customers improve their quality of life. Financial institutions could partner with other service providers such as exchanging bank loyalty points for airport pickups, or automatically ordering groceries for home delivery, or identifying surges in utility bills and then suggesting a financing plan for solar rooftop purchase. These services could be learned from past transactions and predicted by AI.

  1. How or Humanness

Conversational banking evolves chatbots beyond simple FAQ responses. Initially, they are trained with pre-defined prompts and answers. As they progress, they learn industry-specific vocabulary and become more flexible in understanding customer keywords to find relevant information.

Today, the level of analytics has greatly improved. AI will be built and currently have built to be more like humans with the learning of emotions and tone of voice when dealing with customers. Additionally, as the AI learns through multiple conversations, it increases the level of personalization and ability to support individual clients.

For example, in various use cases like cross-selling new products and collecting loan payments, AI will identify the right tone of voice such as softer and higher pitch voice to interact with each customer. Adversely, AI would analyze the emotions of customers during the interaction to identify suitable responses or when to escalate the conversation to the human support team. To drive a personalized experience, servicing channels are supported by AI-powered decision-making, including speech and sentiment analytics to enable automated intent recognition and resolution.

Why Now?

AI adoption poses benefits in terms of cost reduction, improved customer satisfaction, and increased competitive advantage. To stay competitive, embracing AI is no longer a choice, but a necessity. Leading institutions are already leveraging advanced AI to serve customers, empower employees, and secure their market share.

Higher automation reduces costs and improves customers’ satisfaction through operational efficiency, minimizing errors and optimizing resource utilization. Conversational banking further enhances customer experience by streamlining human resource allocation, reducing response times, and improving account access and security, while also reducing fraud. Conversational AI plays a key role by integrating data from various teams. This comprehensive data view empowers institutions to manage resources more effectively and ensure compliance with the evolving market and regulatory landscape.

HSBC’s use of conversational banking serves as a great example. Launched in June 2018, HSBC’s AiDA chatbot is used to respond to clients’ requests via instant messaging, reducing the cost related to calls by 90%. In February 2021, HSBC used a chatbot powered by AI to provide instant pricing and analytics for foreign exchange options, making complex trading more accessible and efficient.

Leading financial institutions are increasing the use of advanced AI technologies. McKinsey’s Global AI Survey reveals that nearly 60% of financial-service sector respondents have already embedded at least one AI capability into their operations. This digital transformation is taking shape through:

  • Conversational bots for basic servicing requests
  • Humanoid robots in branches to serve customers
  • Machine vision and natural-language processing to scan and process documents
  • Machine learning to detect fraud patterns and cybersecurity attacks from conversations

Technology helps speed up the tedious process and push financial institutions ahead of other banks and Fintechs. The new tech adoption will become standard practice or baseline in the eyes of customers.

Consumers are increasingly shifting towards digital channels, favoring mobile banking for simple services, and reducing visits to physical branch. This behavior shifts emphasizes the need for banks to adapt. Mobile banking, pioneered by commercial banks in Thailand, has been a successful driver of the digital trend. Presently, over 80% of bank clients have and regularly use mobile banking applications. In addition, government initiatives such as PromptPay and the G-Wallet policy, aimed at boosting domestic spending, have further accelerated the adoption of digital payments and mobile banking. A 2022 Mastercard survey revealed that a staggering 94% of Thai consumers now use digital payments. With Gen AI gaining popularity in public eyes, commercial banking is expected to play a an increasingly significant role in supporting banks’ customers in the near future.

Non-banking businesses are entering the banking space. Banking business is now embedded in a wide range of software and applications (See more about Embedded Finance here). One significant threats to banks is the emergence of  “Super Apps” (See more about Super Apps here). These Super Apps integrate various financial services, including payments, and in some cases, lending, and insurance, potentially becoming one of the main operating businesses and posing a threat to incumbent banks. They disrupt traditional methods of offering new banking products and services and may soon seek to expand their presence and involvement in financial services on a larger scale. As a result, financial institutions will need to reassess how they participate in digital ecosystems and leverage AI to unlock the untapped data potential for competitive advantage.

Things To Be Cracked

Image generated by Gemini

Conversational AI could help boost banks’ presence and support clients; however, the technology presents challenges and hidden costs. New tech adoption is poised to disrupt how banks manage and operate. Incumbent players face a balancing act: ensuring agility and flexibility for competition while maintaining security and compliance to secure trust as financial service providers.

  1. Infrastructure Readiness:

Implementing conversational banking requires robust computing power and flexibility to support real-time analysis. However, legacy core banking systems are often difficult to modify. Additionally, fragmented data across different teams hinders the analysis of relevant data and timely generation of recommendations. Most importantly, transitioning to a new infrastructure and ongoing computational requirements can incur significant costs.

  1. AI and Talent Management:

A successful AI strategy requires a clear roadmap for both technology and talent. Currently, a major challenge is the lack of standardization in conversational AI adoption. Organizations are still exploring the best ways to integrate technology. The strategy should encompass both the technical process of tech implementation (building, testing, deploying, and monitoring) and talent development to uplift existing employees to develop and maintain new products and services. Ultimately, the strategy must identify and develop use cases where conversational AI can transform customer journeys, leading to defined outcomes such as real-time client support, tailored service with insightful data, improved customer lifetime value, and lowered operating costs.

  1. Regulatory and Ethical Considerations:

Disruptive technologies often raise regulatory concerns, particularly for financial institutions where reputation and stability are critical. This is especially true for conversational AI, which handles sensitive personal and financial information. Personal data management and changing regulations should be closely monitored when adopting conversational AI. Additionally, there are ongoing developments in the framework to help reduce human biases (such as social profiling) imposed on AI models. For example, Thailand’s Electronic Transaction Development Agency (ETDA) has published AI Governance Guidelines with hopes of bolstering domestic ethical AI development and adoption.

Closing Thoughts

Conversational AI offers numerous benefits in enhancing the customer journey. However, as with any technology, it comes with associated costs. Financial institutions should be aware of the limitations of AI technology and carefully balance the costs and benefits of adopting it. Ultimately, the success lies in the hands of banks that could identify where best to implement new technology and where its implementation might not be the most suitable solution.

 

Author: Panuchanad Phunkitjakran (Pook)

EditorsSupamas Bunmee (Jae), Woraphot Kingkawkantong (Ping)

Potential ESG Impacts in Startups Created by Early-Stage Minority Investors

Posted on by [email protected]

ESG is no longer just a buzzword but is now widely known to everyone, primarily driven by the urgency to address global warming, social issues, and corporate governance failures. Despite initiatives developed by various parties, ESG challenges persist, creating opportunities for venture capitalists (VCs) to play a crucial role. With a unique investment approach, VCs are well-positioned to support early-stage or unproven innovations that address ESG concerns not only with capital but also with resources such as knowledge, tools, and networks. Therefore, this article will explore the impacts on ESG that VCs can contribute to our society.

Impacts on ESG Driven by angel investors and incubators

There are several roles for the early-stage minority investors to play in guiding startups towards ESG, however, their influence admittedly varies depending on the nature and timing of their involvement. For instance, angel investors, through seed funding and close relationships, can help nascent startups connect with resources and like-minded individuals for impact-driven growth. This early guidance on ESG integration potentially lays a strong foundation. However, their smaller investments and shorter investment horizons often constrain their ability to significantly influence long-term impact during the critical early stages where startups may prioritize survival over sustainability. Similarly, incubators normally provide valuable strategic support and have a powerful impact on a startup’s business model. However, their focus on the initial stages often means they disengage before startups reach larger funding rounds and achieve more substantial impact efforts. To truly foster sustainable practices and long-term impact, collaboration between early-stage incubators/ investors and later-stage investors such as growth-stage venture capitals, with larger capital and resources as well as longer engagement horizons, becomes crucial.

Impacts on ESG Driven by Venture Capital

This section will delve into the ESG impact driven by VCs across three dimensions based on the significant impact VCs can create and the urgency to address pressing issues: Environment, focusing on accelerating carbon emission reduction; Social, examining impacts on workplaces and opportunities for change; and Governance, assessing the influence on startup governance and sustainable success. Each angle will highlight how VCs can contribute to organizational changes within startups and address broader issues through innovative funding initiatives.

  • Environment: Accelerating Carbon Emission Reduction

As we all acknowledged from the Paris Agreement, to limit global warming to 1.5°C, emissions need to be reduced by 45% by 2030 and reach net zero by 2050 [1]. More than one-third of the emissions reduction in 2050 requires innovative technologies that are currently under development, according to the International Energy Agency’s (IEA’s) net-zero scenario [2]. A significant portion of these technologies is still in their early stages, not yet market-ready, too costly to manufacture, or unproven at scale. Thus, substantial amounts of innovation capital will be crucial for decades to come. This need aligns with the nature of VC investments, which primarily seek investment opportunities in early-stage companies with significant growth potential while typically holding smaller equity stakes. Additionally, the relatively small investment size from VCs creates greater opportunities for many startups to secure funding, increasing the likelihood of successfully developing and scaling underdeveloped innovations. This unique approach positions venture capitalists as key enablers for the development of groundbreaking technologies to be commercially proved before attracting larger public market fund flows in the long run. At the same time, VCs tend to shift their investment focus towards industrial sectors which require higher- Emissions Reduction Potential (ERP) technologies such as carbon capture, utilization and storage (CCUS) and green hydrogen, according to PwC’s State of Climate Tech 2023 report [3]. This shift anticipates significant reductions in carbon emissions in the near future, aligning with the 1.5°C path.

In addition to supporting climate tech startups, VCs can play a pivotal role by promoting awareness and encouraging their portfolio companies to actively monitor and openly disclose their environmental performance metrics because numerous technology startups, particularly those in the crypto space and AI sector with high energy consumption, contribute significantly to carbon emissions. This proactive approach holds these companies accountable for their progress, fostering transparency and facilitating impact-conscious investors in making well-informed investment decisions.

  • Social: Creating Job Opportunities and Better Workplaces

The venture capital industry remains trapped in a diversity deficit, primarily led by white, elite-educated men and heavily concentrated in leading universities. According to the Stanford Social Innovation Review, this homogeneity tends to influence VC funding decisions, as the statistic shows that a staggering 86% of VC dollars in the US flow to male-only founder startups [4]. This issue holds significant importance, especially considering the substantial role startups play in job creation, contributing to nearly 1.7 million job gains in 2019 [5]. As a key driver for job creation, venture capitalists possess a considerable opportunity to instigate change and address this imbalance. Fortunately, change is underway as VC funds like MaC Ventures and ImpactX are challenging the status quo, focusing on supporting founders who came from marginalized demographic or minority background, while initiatives like AllRaise champion female leadership and advocate for doubling the share of capital controlled by women in VC by 2030 [4].

In addition to this, venture capitalists have the potential to drive substantial change not only in addressing social issues within workplaces but also in tackling broader societal problems, such as education inequity and financial inclusion, through funding provided to startups that develop solutions for such social problems. In the Beacon VC’s latest article, How Socioeconomic Status Affects Thai Education Inequity and How Stakeholders in the Community Can Address It, various edtech companies, including Ookbee, SchoolBright, and Open Durian, are highlighted for their contributions to addressing education inequity primarily stemming from socioeconomic disparities [6].

  • Governance: Driving Positive Cultures and Sustainable Success

Similar to nurturing children, the earlier the nurturing process commences, the more effortless it becomes to shape their foundational beliefs and habits. Venture capitalists play a pivotal role in establishing strong governance cultures, values, and behaviors within startups during their early days, preventing undesired actions or mindsets from becoming ingrained and resistant to change as the companies scale. Theranos, a medical technology startup, initially reached a valuation of $9 billion, but subsequently plummeted to $800 million. The decline happened because the founders intentionally presented fake medical testing and exaggerated the company’s profits to attract funding from investors. This case also brought about serious issues in corporate governance due to insufficient oversight and a board filled with close allies instead of independent voices. Similarly, GoMechanic, a car servicing and repairing platform initially valued at close to $700 million, experienced a significant valuation-drop due to reported over-inflated numbers and fictitious garages. Ultimately, it was sold for just $30 million [7]. The undeniable correlation between corporate governance and valuation emphasizes the crucial roles of investors and boards in driving sustainable value creation in a startup’s early days.

Furthermore, good governance is vital for startups to access essential capital sources from both private and public markets. Since governance has long been the main focus for private equity firms when considering investing in the company due to its significant impact on risk management and strategic decision-making. Additionally, the stock market listing process has evolved, with Nasdaq imposing new board diversity requirements, further emphasizing the growing importance of governance in the startup landscape.

In addition to fostering good governance within an organization, VCs also drive the emergence of new tools for regulators, including auditing firms, government agencies, and internal compliance teams, to validate and ensure the accuracy and compliance of disclosed information. This contributes to the overall enhancement of good governance within the ecosystem. Startups such as Mindbridge AI, 6clicks, Trunomi, and ClauseMatch assist enterprises in better managing risks and staying compliant with evolving regulations.

How VCs Play a Role in Shaping Companies’ ESG Pathway

VCs are playing a crucial role in shaping companies’ ESG pathways by integrating ESG considerations throughout the entire investment lifecycle from initial screening and due diligence to deal documentation, ownership period, and eventual exit. This integration highlights a commitment to responsible investing, where financial success aligns with positive contributions to the broader global communities and environment.

  • Implement ESG in Initial Screening

One of the most common practices that VCs opt for ESG evaluation during the screening process is having exclusion lists or specific deal-breaker criteria. The examples of exclusion lists are any companies on EU, UK, USA, or UN sanctions lists or those violating UN conventions and declarations on human rights or engaging in illegal activities according to Elevator Ventures’ screening criteria [8]. Some VCs like Astanor evaluates ESG risks in potential portfolio companies before investment.. For instance, environmental criteria involve avoiding highly polluting industries. Social criteria include avoiding dangerous substance handling that could jeopardize employee safety and surrounding communities. Governance criteria focus on avoiding operations in high-risk countries for money laundering, terrorism financing, or corruption, while ensuring good corporate governance [9].

Deal-breakers may arise from ethical concerns, such as indications of greenwashing or misleading environmental impact claims. For example, VCs may hesitate to invest if a climate tech startup lacks a clear and measurable impact on addressing climate challenges. Tangible results and a well-defined mission become crucial factors in the investment decision-making process.[10].

  • Implement ESG in Due Diligence

During the due diligence process, assessments can occur informally through methods like observing founders’ behavior, or more formally through ESG workshops and questionnaires.

In informal assessments, as revealed in a survey by PRI, some investors gauge founders’ values and ethics by observing their behavior in social settings, such as restaurants [11]. This practice helps identify potential concerns, such as misogynistic behaviors, which may impact the startup culture and subsequent business. For more formal methods, in investment rounds led by Atomico, a conscious scaling workshop is conducted with each new portfolio company as part of the final due diligence process [12]. This workshop involves collaborative sessions between founders and investors/the board, with a focus on identifying and mitigating long-term risks associated with the business model or the technology’s impact on society, the environment, and other stakeholders. Elevator Ventures employs a verification approach for potential portfolio companies using ESG questionnaires, ensuring alignment with regulatory frameworks like Sustainability Accounting Standards Board (SASB) standards.

  • Implement ESG in Deal Documentation

Deal documentation reflects the commitment of startups to ESG principles. Agreements may include clauses that bind startups to certain ESG standards and practices including specific milestones related to the reduction of carbon emissions or the implementation of sustainable practices within the company. For instance, during investment negotiations with potential investees, Astanor uses their commercially reasonable efforts to embed ESG requirements in contractual documents signed by the Fund Manager to secure full alignment with Astanor’s ESG and impact ambition. Atomico requires its new portfolio companies, where it leads an investment round, to design and implement a Diversity & Inclusion Policy within three months and a Diversity & Inclusion strategy within six months of investment. Similar to Beacon VC, their portfolio companies under the impact investment mandate are required to reach an agreement with the VC on the ESG metrics outlined in the investment agreements. These metrics will be regularly tracked, and the portfolio companies will provide reports to Beacon VC in accordance with the agreed-upon terms.

  • Implement ESG during Ownership

 How a VC engages with portfolio company management during ownership depends on its investment strategy and governance model. For example, VCs may aim to oversee invested startups and actively participate in voting on ESG matters by securing a board seat, often requiring their leadership in the investment round or holding a larger share than other participating investors in the same round. Despite VCs holding a minority share, being early investors in a company provides a unique advantage by fostering a closer relationship between VCs and the founder and team. This closeness aids in cultivating an ESG mindset among them. Accordingly, how VCs can drive ESG implementation in invested companies is defined through two activities: Engagement and Voting rights.

  1. Engagement

Collaborating on ESG Program Development: VCs can work with portfolio companies to establish an ESG program, involving tasks such as drafting a policy, assigning responsibility for ESG operations, and setting up processes to manage ESG activities. To begin the ESG journey, ESG_VC has developed a standardized 48-question ESG questionnaire for early-stage companies, applicable from Seed to Growth stages and across both B2B and B2C sectors [13]. It provides a tangible ESG score and identifies key areas for startups to improve ESG performance. The Astanor Team has conducted an Impact Deep Dive within six months after investment [14]. This deep dive aims to establish the baseline for both ESG and impact, facilitating the development of a constructive ESG roadmap and the identification of the most suitable impact KPIs for the company.

Promoting Knowledge Sharing on ESG: Given the absence of standardized ESG incorporation practices within the startup industry, startups are facing with a continuous need to stay informed about the ongoing developments and emerging knowledge in this dynamic field. Venture capitalists have a potential to leverage expertise and experience on ESG matters across the portfolio by encouraging sharing of knowledge and good practices among different companies. For instance, a general partner could organize periodic meetings or conferences with representatives from all of its portfolio companies or startups in the ecosystem to discuss ESG topics. As an example, Beacon VC has partnered in creating a community known as Climate Tech Club, providing a space for startups and individuals who passionate about ESG transition to share knowledge and stay updated on the latest ESG information. The community facilitates ongoing knowledge-sharing sessions and workshops throughout the year, all are open for participation at no cost. An upcoming event in February is the ESG Essential Workshop, designed to guide startups through the practical steps of initiating an ESG report.

  1. Voting Rights

Board of Director role: Venture Capitalists with Board seats in startups are well-positioned to influence ESG considerations. This influence can take the form of Board Resolutions and the delineation of veto powers, which can be explicitly addressed and mutually agreed upon during the initial investment documentation phase. Subsequently, if any issues of ESG concern emerge, the VCs on the Board have the authority to exercise their voting rights, either in favor of or against such matters. Additionally, in case where concerns are raised by shareholders, the Board is expected to take action by engaging with other shareholders. Failure to respond to shareholders’ concerns could be perceived as a governance failure on the part of the Board.

Shareholder Resolutions: In case VCs do not have a Board seat, they also have the option to propose resolutions compelling companies to address specific ESG concerns. While not always successful, these resolutions draw attention to critical issues and may motivate companies to take action to avoid adverse publicity. Given the growing demand for ESG information in corporate financing and from investors, including private equity firms, banks, and other capital providers, there is indirect pressure on startups to consider ESG as part of their business goal. This role helps investors, irrespective of the shares they hold, in holding companies accountable for their actions and demanding change when necessary.

  • Implement ESG during Exit

The survey from Deloitte revealed that US Private equity investors are nearly three times as likely as corporates to approach ESG due diligence consistently and formally, and nearly twice as likely to include ESG clauses in M&A contracts [15]. Furthermore, Nasdaq recently introduced new board diversity requirements for listed companies. This development indicates a notable advancement in ESG considerations across financial markets, particularly among potential buyers like private equity firms and stock exchanges. As a result, VCs can play a key role in ensuring portfolio companies meet these standards.

Barriers to ESG Implementation at Each Investment Process

Despite the clear intention of VCs to incorporate ESG considerations into their investment process, several challenges arise due to factors such as limited data and standardized metrics, subjective interpretations of ESG criteria, and the varying readiness of startups. The following points highlight the complexities VCs face in aligning investment strategies with ESG practices.

  • Challenges in Initial Screening and Due Diligence:
    1. Data Reliability and Standardized Metrics: The availability of reliable and standardized ESG data remains a hurdle for VCs. The challenge lies in verifying the accuracy of data sourced solely from the company, without third-party verification. The absence of universally accepted metrics further complicates the consistent assessment of potential investments’ ESG performance.
    2. Subjective interpretations: ESG data often includes qualitative information such as, stakeholder engagement practices, supply chain ethics and labor standards, leading to variations in interpretation among individuals. Differing perspectives on what constitutes strong ESG practices may create ambiguity in the evaluation process.

 Challenges in Deal Documentation and Ownership:

    1. Readiness of Startups: Not all startups are equally prepared to align with ESG principles. VCs may encounter resistance or limited ESG infrastructure in some companies, requiring additional efforts to bring them in line with the desired sustainability goals.
    2. Contractual complexities: Embedding ESG clauses in agreements can be complex, requiring careful consideration, legal expertise, and skilled negotiation. For instance, Finding the right balance between specific targets and flexible frameworks can be challenging. Too specific clauses may hinder adaptability, while overly broad ones lack accountability. Defining clear consequences for non-compliance with ESG clauses requires careful consideration of proportionality and unintended impacts.
    3. Limited influence in later stages: As startups progress through funding rounds, the power dynamics between VCs and founders often shift, and the stakes that VCs hold normally decrease in later rounds, reducing their control over ESG implementation. For example, a VC may no longer have enough voting power to actively influence board decisions or push for specific ESG measures.

By recognizing the challenges that VCs may face during the process of embedding ESG practices into their investment processes or within the startup culture, VCs need to take into account the stage of startups and balance proactive ESG management to avoid unrealistic expectations. It is crucial to recognize the need for startups to concurrently achieve financial viability and expand their business operations. Accordingly, there is a roadmap suggested by ESG_VC, stating when ESG implementation should be reasonably initiated at each stage of the company.

How Other Capital Providers Drive Sustainable Startup Growth

While venture capitalists play a crucial role in shaping a startup’s early culture and governance, fostering long-term success involves a symphony of financial players, each with their own unique instruments. Here are how other capital providers can contribute to creating sustainable and impactful startups:

  • Financial Institutions:
    1. Corporate Financing: Beyond traditional loans, financial institutions can offer innovative financing solutions like sustainability-linked loans, where interest rates are tied to the company’s achievement of ESG goals. This financial product incentivizes startups to integrate sustainability into their core business model. Additionally, financial institutions can provide special loans to consumers for ESG projects with reducing interest rates. For example, KBank launched the “Solar Save” campaign, presenting a 0% interest rate for the first four months on solar PV installation projects and maintaining a low interest rate of 3.75% for the initial four years [16].
    2. Tools and Education: Financial institutions can assist startups by leveraging their resources and corporate networks to educate them on ESG trends and regulations. They can also provide essential tools such as ESG or carbon management platforms, facilitating the initiation of ESG measurement within organizations. These proactive approaches empower startups to make well-informed decisions that align with greener practices. In terms of education, KBank organized the “Earth Jump 2023” seminar, bringing together leaders from both the public and private sectors to discuss and exchange perspectives on fostering “sustainability” as a catalyst for business growth [17].
  • Other Stakeholders in Public Markets:
    1. Sustainability Indices: Stock exchanges provide alternative fundraising avenues for startups with strong ESG commitments. Stock Exchanges may introduce specialized platforms or indices that track the performance of companies with strong ESG commitments, attracting investors seeking impact alongside financial returns.
    2. ESG requirements for listing in Stock Exchanges: Stock exchanges may implement stricter listing requirements that prioritize ESG principles and responsible governance practices, encouraging transparency and sustainability among publicly traded companies.
    3. Client-led Voting for ESG Matters: Asset management firms provide an alternative approach for investors who invest via the firms to have a say in vital company matters like mergers, acquisitions, director elections, and ESG matters. Traditionally, the responsibility of voting has been reserved for asset managers, in the realm of ESG concerns, asset managers’ investor-clients are demanding an increasing say on companies’ ESG matters. As a result, Blackrock, for instance, plans to broaden this right to all clients, and it is piloting the expansion of voting rights in the UK. Other asset management firms such as Vanguard and DWS Group, are also embracing this trend [18].

Closing Thought:

In the dynamic landscape of sustainable investing, early-stage minority investors, especially VCs, serve as pivotal architects, steering startups towards impactful ESG pathway. Their collaborative efforts contribute beyond capital injection, offering innovative financing solutions, education, tools, and alternative fundraising avenues, fostering a holistic approach that resonates with sustainable investing. However, for sustained impact, this orchestration requires collaboration with other investors and stakeholders, including those who come later and the management within the company. The long-term journey to sustainable startups necessitates a continuous symphony of efforts from diverse stakeholders, ensuring positive change extends far beyond the early stages.

 

Sources:

[1]  https://www.un.org/en/climatechange/paris-agreement

[2]  Executive summary – Net Zero Roadmap: A Global Pathway to Keep the 1.5 °C Goal in Reach – Analysis – IEA

[3]  https://www.pwc.com/gx/en/issues/esg/state-of-climate-tech-2023-investment.html#fewer-early-stage-deals-but-a-steady-inflow-of-first-time-investors

[4] https://ssir.org/articles/entry/how_venture_capital_can_join_the_esg_revolution

[5] https://www.bls.gov/spotlight/2022/business-employment-dynamics-by-age-and-size/home.htm

[6]  https://www.beaconvc.fund/research/how-socioeconomic-status-affects-thai-education-inequity-and-how-stakeholders-in-the-community-can-address-it

[7] https://www.financierworldwide.com/ingraining-good-governance-in-start-ups-buck-stops-with-the-investors

[8] https://www.elevator-ventures.com/unpri-esg-policy/

[9] https://astanor.com/wp-content/uploads/2022/12/SFDR-Disclosure-GHVI-S-LP.pdf

[10] https://www.eu-startups.com/2023/10/what-vcs-look-for-in-climate-tech-startups-opportunities-and-deal-breakers/

[11] https://www.unpri.org/private-equity/starting-up-responsible-investment-in-venture-capital/9162.article

[12] https://atomico.com/esgpolicy

[13] ESG_VC (esgvc.co.uk)

[14] SFDR-Disclosure-GHVI-S-LP.pdf (astanor.com)

[15] https://www2.deloitte.com/us/en/pages/about-deloitte/articles/press-releases/private-equity-leads-corporate-deal-teams-on-esg-in-mna.html

[16] https://www.kasikornbank.com/th/news/pages/solarsave.aspx

[17] https://earthjump-thailand.com/#about

[18] https://professional.ft.com/en-gb/blog/investors-demand-greater-esg-voting-rights/

 

 

Author: Supamas Bunmee (Jae)

Editors: Warittha Chalanonniwat (Paeng), Wanwares Boonkong (Pin), and Woraphot Kingkawkantong (Ping)

How Socioeconomic Status Affects Thai Education Inequity and How Stakeholders in the Community Can Address It

Posted on by beaconvcadmin

Image by UNESCO Bangkok

The playing field of education shouldn’t be tilted by wealth, but in a world where socioeconomic status (an individual’s social standing based on economic status) casts a long shadow, it often is. While differences in race, gender, or nationality can shape life trajectories, disparities in income paint an even starker picture. In Asia-Pacific, according to Asia-Pacific Social Science, for instance, the richest 25% of households enjoy opportunities 13 times greater than the poorest 25%. Enter the DEI (Diversity, Equity, and Inclusion) movement, a beacon of hope aiming to bridge such divides. But what does DEI look like in a country like Thailand?

Here, the education gap reigns supreme. FleishmanHillard Research (2023) found it the top DEI priority. Thailand’s educational landscape is booming. International schools sprout like mushrooms, even going public, while top schools boast cutting-edge tech classes like blockchain and AI. Yet, only those with deep pockets can access this gilded future, evidently shown by Thai students’ very low on PISA index in every factor. This ironic reality – where advancement widens the gap instead of closing it – demands immediate attention.

This article delves into the heart of this matter, dissecting how socioeconomic status breeds educational disparities, followed by our thesis of how we can collectively address these disparities. Then, we will also make distinction between two important concepts, Education Inequality and Education Inequity, and argue that solving Education Inequity is most paramount. We’ll explore the role of EdTech, a potential equalizer, and alongside other diverse stakeholders can collaborate to bridge the educational divide. Join us as we embark on this critical journey, where the future of Thailand’s children hangs in the balance.

 

What Is DEI and What Is Its Relevance To Education?

Diversity, Equity, and Inclusion (DEI) is a part of the ESG movement, specifically the Social part, that aims to create a world where everyone is equally worthy, able to strive, and lives in harmony despite all differences. Though the concept of DEI originated from the issue of race and gender, it has been developing to cover all other aspects including education, political beliefs, and socioeconomic status. Let’s get to know each component:

  • Diversity: Acknowledging the richness of human variation, encompassing not just visible traits like race and ethnicity but also invisible factors like socioeconomic background and educational attainment.
  • Equity: Leveling the playing field by providing targeted support and resources to bridge the gap between different groups. This goes beyond equal access to ensuring equal outcomes.
  • Inclusion: Creating a sense of belonging and value for everyone, regardless of their background. This fosters a sense of community and empowers individuals to contribute their unique perspectives.

By understanding these interconnected elements, we can see how DEI directly addresses the challenges of education equity, urging us to recognize the individuals’ different background and circumstances (e.g., socioeconomic status) and provide equitable resources to ensure the same educational outcome. It’s about dismantling barriers and fostering a system where every student, regardless of their socioeconomic status, has the opportunity to reach their full potential.

 

Beyond Equality: Why Education Equity Among Socioeconomic Status is Thailand’s DEI Imperative

While the DEI movement in the West often focuses on race and gender, in Thailand, it takes a different form. As FleishmanHillard research (2023) reveals, a staggering 32% of Thai people identify education inequality as the most pressing DEI concern, placing it at the pinnacle of the DEI issues that need to be addressed. This is no mere coincidence.

Source: FleishmanHillard Research, 2023

While “education equality” aims to provide equal resources to all students, it doesn’t guarantee equal outcomes. This is where “education equity” steps in. It strives to ensure that despite differing backgrounds, all students reach similar educational benchmarks and are equipped to compete in the job market and have an equal chance for social mobility.

Think of it this way: providing every student a book (equality) is meaningless if some lack the support or environment to read effectively (equity). Education equity addresses these disparities by offering targeted resources and support, such as scholarships and financial aid workshops, specifically for students from low-income families.

Source: McGraw Hill PreK-12

In fact, when we take a look at what factors prevent Thailand from achieving education equity, research by Asia-Pacific Social Science Review (2022) reveals that while various factors like language, disability, and location contribute to education inequity, socioeconomic status consistently ranks as the most impactful component in Thailand. The parents’ socioeconomic status has played a significant role in children’s opportunities in higher education. This critical issue deserves attention for two key reasons:

  • Sizable Affected population: According to KKP research (2021), the richest 10% own over 77% of the country’s wealth. Given such a high level of wealth disparity, a significant portion of the population is struggling to afford quality education for their children.
  • The Persistent Loop of Poverty: Limited education often leads to lower income, perpetuating the cycle of poverty. As the Organisation for Economic Co-operation and Development reports, a university degree can result in wages nearly 2.5 times higher than a lower secondary degree. Without education equity, this gap widens with each generation, trapping individuals in a cycle of disadvantage.

In summary, achieving education equity among socioeconomic status is not just a moral imperative; it’s an economic necessity for Thailand’s future.

 

Unequal Playing Field: Navigating Education and Employment by Socioeconomic Status

Socioeconomic status casts a long shadow on Thai education and employment opportunities, creating distinct tiers with varying access to resources and success. While acknowledging the complexity of such categorizations, we can broadly divide Thai society into three segments based on their educational and economic realities: the Privileged, the Mainstream, and the Strugglers.

The Privileged: This segment enjoys abundant resources and opportunities. Their families can afford quality education, extracurricular activities, and skill development, often equipping them with advanced qualifications and specialized knowledge. This translates to access to high-paying jobs in professional fields and the potential to further accumulate wealth.

The Mainstream: This segment comprises a significant portion of the population with sufficient resources to attain basic education and essential skills. They are actively engaged in the job market, securing skilled positions and earning enough to cover their needs. While financial security is attainable through hard work and dedication, upward mobility within this group can be challenging.

The Strugglers: This segment faces significant economic hardship and limited resources. Meeting basic needs consumes their energy and income, leaving little room for education or skill development. They often rely on low-paying jobs with minimal opportunities for advancement, perpetuating a cycle of poverty. This lack of access to quality education and resources severely hinders their ability to break free from this cycle.

 

The Urgency of Equity: Empowering the Strugglers

While all groups navigate challenges, the Strugglers face a unique predicament. Without external support, their ability to break the cycle of poverty through education is severely restricted. To put this simply, they lack the means to access the tools needed for upward mobility on their own.

By focusing on bridging the educational gap for the Strugglers, Thailand unlocks the potential of a large segment of its population. This, in turn, fosters a more equitable society with a broader tax base, increased productivity, and a more just distribution of wealth. Ultimately, investing in the Strugglers is not just an ethical imperative, it’s a strategic investment in the future of Thailand.

 

The Path to Equity: A Three-Pronged Approach for Thailand’s Strugglers

Bridging the educational gap for Thailand’s Strugglers requires a multi-faceted approach that tackles the Strugglers’ unique challenges. Here, we propose a three-pronged strategy involving various stakeholders to pave the way for educational equity:

  1. Freeing the Strugglers from Financial Burden: Kick-starting Successful Learning Journey

The financial hardship casts a long shadow on a Struggler’s educational journey. Parents grapple with the impossible choice between immediate survival and investing in their children’s future. This burden manifests in several ways such as child labor and parental pressure for children to contribute financially, and limited ability to afford financial resources. To address this issue, there are several potential areas to be addressed such as:

  • Targeted financial assistance: Scholarships, grants, and loan forgiveness programs, either channeled directly to the families or schools, specifically designed for Strugglers can alleviate the immediate financial pressure of school fees, uniforms, and educational materials.
  • Conditional cash transfers: Providing financial assistance directly to families, on the condition that their children attend school regularly, can incentivize education and reduce child labor.
  • Subsidized childcare and after-school programs: Freeing up parents’ time by providing affordable childcare and after-school programs can allow them to work without sacrificing their children’s education.
  1. Uplifting the Landscape: Building Equitable Learning Environments

The next step is to address the disparities in educational resources and infrastructure. This requires a concerted effort to ensure Struggler schools are equipped to provide quality education on par with the Mainstream. This disparity manifests in several ways such as teacher quality, limited and out-of-date equipment and facilities, lack of community support, and obsolete curriculum and teaching materials. To bridge these disparities, below are some areas that would benefit from immediate intervention:

  • Targeted investment in rural and underserved schools: Increased funding and resource allocation specifically for schools catering to Strugglers can ensure they have access to qualified teachers, modern technology, and up-to-date resources.
  • Teacher training and support: Providing ongoing training and professional development opportunities for teachers in underserved communities can equip them with the skills and knowledge necessary to effectively support Strugglers’ learning.
  • Curriculum reform: Integrating real-world skills and relevant job market trends into the curriculum such as coding, basic technology knowledge like Blockchain and AI, or sales and presentation skills, can prepare Strugglers for future success and make learning more meaningful.
  1. Empowerment and Personalization: Tailoring Education to Individual Needs

At the heart of any successful learning journey lies a strong internal drive to learn and succeed. For Strugglers, it is often hard to imagine life beyond the status quo given their limited exposure to role models and information about diverse career paths. Additionally, witnessing their parents’ struggles can lead to self-doubt. Negative experiences or societal stereotypes can also lead to feelings of inadequacy, hindering Strugglers’ belief in their ability to achieve their goals. Below are some solutions that can address the issue:

  • Mentorship and career guidance: Connecting Strugglers with mentors from similar backgrounds or experienced professionals can provide invaluable advice, role models, and networking opportunities, helping them navigate career choices and access job markets.
  • Internship Opportunity: Providing Strugglers with a field to exercise their classroom knowledge in real-life situations not only strengthens their skills but also increases their recruiting opportunities.

Educational equity demands a move beyond one-size-fits-all approaches. Each Struggler student has unique goals, learning styles, and aspirations. This diverse landscape requires personalized learning pathways based on their learning style and goals, personalized mentorship and career guidance, and targeted skill development programs suited for excelling in the job market.

  • Adaptive learning platforms: These platforms personalize learning pathways based on individual student progress, strengths, and weaknesses, ensuring efficient knowledge acquisition and catering to diverse learning styles.
  • Micro-credentialing and skills-based learning: Offering bite-sized, skill-focused courses allows Strugglers to acquire relevant skills in short periods, even if they cannot pursue full-time degrees. This can be particularly helpful for those seeking immediate employment opportunities.

 

Building Bridges, Not Walls: A Collaborative Approach to Education Equity in Thailand

Bridging the educational gap for Thailand’s “Strugglers” demands a collective effort, not a solitary sprint. Each stakeholder in the education ecosystem plays a crucial and unique role in dismantling barriers and building a future where every child, regardless of background, has the chance to thrive. The discussion below provides a general frame of thought on how each stakeholder could mainly contribute. Much of what is being described below has already been done sparsely and uncoordinatedly, but Thailand as a nation can do so much better to ensure equitable education for the Strugglers.

Governments act as architects of supportive infrastructure. Firstly, infrastructure can be leveled by equitable resource allocation, either in the form of fiscal budget allocation or tax incentives for other stakeholders to contribute, ensuring that Strugglers in rural and underserved schools have access to qualified teachers, modern technology, and up-to-date resources. For more thoughts on closing the digital inequality, please visit Beacon VC’s article here. Secondly, infrastructure can be future-proof by integrating real-world skills like coding and AI into the curriculum preparing Strugglers for the job market and making learning more relevant to their aspirations. Lastly, infrastructure can be more inclusive by implementing programs that provide financial assistance to families in exchange for their children’s school attendance can incentivize education and reduce child labor. This requires close collaboration with social welfare ministries and community organizations for effective implementation.

Financial institutions act as fuel for change. Leveraging the financial capability, access they have to Thai communities, and the amount of human resources they have, financial institutions can catalyze the transition at both macro and micro levels.

At the macro level, financial institutions can join hands with several stakeholders, such as government and NGOs, to structurally build equitable education systems, through targeted scholarships and loans designed specifically for Strugglers, families can prioritize education without sacrificing immediate needs. Additionally, financial institutions can also channel investments into areas that would advance solutions tailored to Strugglers’ unique challenges, such as EdTech’s affordable learning platforms, adaptive online learning technologies, or micro-loan programs for schools. Financial institutions can also play an active role in shaping financial literacy for Strugglers about budgeting, saving, and responsible credit management can empower them to make informed financial decisions regarding their children’s education.

At the micro level, financial institutions can have a direct and profound impact on individual Strugglers who have the potential to excel. Through specially designed initiatives for Strugglers like internship/ apprenticeship programs or mentorship and career counseling programs, in partnership with local schools or vocational institutions, Strugglers can get inspiration and obtain relevant skills within the field and inspiration to push their career forward. Inversely, financial institutions will have direct access to a talent pool that is trained specifically for their unique organizations’ business and operational requirements.

NGOs and surrounding communities act as networks of support. At the national or municipal level, using their collective voice, NGOs and communities can advocate for the awareness of Struggler’s situation and raise public support for policy changes. At a community level, providing affordable daytime childcare and after-school programs can free up parents’ time and allow them to work without sacrificing their children’s education. Lastly, at the individual level, there’s also an opportunity for mentorship and career guidance programs to connect Strugglers with mentors from similar backgrounds or experienced professionals, providing invaluable guidance and role models.

EdTech startups act as architects of personalized and accessible learning. At the heart of education equity, there’s an important recognition that all students learn differently, at a different pace, and for different purposes.

On one hand, EdTech startups are well equipped to address this through the ability to tailor learning experiences down to different individuals using AI/ML in their adaptive learning platforms, tailoring courses based on individual strengths and weaknesses. Micro-credentialing and skills-based learning allow Strugglers to pick-and-choose relevant skills to acquire in short periods, even if they cannot pursue full-time degrees.  On the other hand, EdTech startups can also assist schools to partially overcome resource constraints in teaching or tailoring students’ education pathways, starting from solutions as fundamental as helping teachers track their students’ homework to tools to run remote classrooms for students in hyper-rural areas.

By working together, each stakeholder becomes a vital link in the bridge, not a barrier on the path. Only through collaborative action can we dismantle the walls of inequality and build an education system that truly empowers Strugglers to reach their full potential. In the next section, we’ll zoom in on the Thai EdTech landscape, examining specific examples of how these innovative tools can tailor learning, dismantle barriers, and empower Strugglers on their path to success.

 

Bridging the Gap: How EdTech in Thailand Can Contribute Through Personalized and Accessible Learning

Source: @terrynut, Medium

Edtech in Thailand has been expanding in line with global trends, reflected by the rise in number of users especially after the Covid-19 period. Digital learning platforms and e-learning solutions were becoming increasingly popular, offering a range of subjects and flexible learning options. This aligns with the findings of a survey conducted by Kasikorn Research Center in April 2021, which found that 96% of respondents anticipated a higher inclination towards using EdTech and online learning. This is especially true for regular employees aiming to enhance their skills and make productive use of their free time.

Riding the boom, EdTech startups have the potential to play a crucial role in achieving education equity, particularly for students facing socioeconomic disadvantages, through 1) Uplifting the Landscape: Building Equitable Learning Environment, and 2) Empowerment and Personalization: Tailoring Education to Individual Needs. Let’s revisit the framework for education equity and explore how EdTechs are already tackling the issue:

 

Uplifting the Landscape: Building Equitable Learning Environment

  • Democratize learning Materials: Ookbee‘s digital content platform makes reading materials more accessible and affordable for students from various backgrounds.
  • Enhanced Learning Management Systems: SchoolBright empowers educators with tools for managing virtual, hybrid, and in-person classrooms, improving accessibility for students in rural areas.
  • Teacher upskilling: Inskru is an online platform that aims to connect, inspire, upskill, and empower teachers across Thailand on various topics like coursework management, in-class activities, and student engagement. Starfish Labz curates short courses that aim to equip teachers with tip and tricks to be more effective in the classroom.

Empowerment and Personalization: Tailoring Education to Individual Needs

  • Tailored Online Career Counseling: Platforms like WE Space and Dynamic School Thailand guide students towards informed career choices by offering assessments and suggesting opportunities aligned with their interests and strengths. They also provide access to relevant courses and workshops, fostering a real-world understanding of career paths.
  • Bite-size Online Learning: Platforms like OpenDurian offer affordable online tutoring by connecting students with qualified tutors, regardless of location. Skillane and FutureSkill cater to diverse needs by providing access to up-to-date subjects not always available in schools.
  • Learning Analytics for Personalization: BrightBytes leverages data on student performance and engagement to provide personalized learning experiences, identify individual needs, and track progress, offering valuable insights to educators. Starfish Class helps teachers identify unique talents and potentials of the students to be able to support accordingly.
  • Internship Opportunity Platforms: เด็กฝึกงาน and JobsBD connect students with internship and job opportunities across industries, allowing them to gain practical experience and explore career options.

 

While EdTech has the potential to revolutionize education and promote equity, its journey in Thailand encounters 2 major challenges that hinder its widespread adoption:

  1. Familiarity with traditional teaching methods. Resistance from schools is rooted from the concerns about difficulty in integrating technology into existing curriculum and training teachers in new methods. Thus, they decide to stick with familiar traditional approaches.
  2. Budget constraints. Schools, especially public ones, struggle with the initial and ongoing costs of acquiring and maintaining hardware, software, and internet infrastructure. This burden extends to individual families, who may not be able to afford subscriptions or devices that would grant access to EdTech solutions.

These challenges highlight the need for a collective effort to bridge the education gap. Governments must invest in infrastructure and training, schools need to embrace innovation, and EdTech startups must offer affordable solutions. This collaborative approach is crucial for EdTech to effectively transform classrooms and empower students from diverse backgrounds, paving the way for educational equity in Thailand.

 

Closing Thought: A Future Built on Equity, Not Equalities

Education inequity casts a long shadow in Thailand, yet a collective yearning for change pulsates beneath. The chasm between the Privileged, Mainstream, and Strugglers reveals a stark truth: education is not a mere ladder, but a complex ecosystem demanding equal outcomes, not just inputs.

EdTech emerges as a beacon of hope in this landscape. Its potential to personalize learning, bridge access gaps, and dismantle socioeconomic barriers can rewrite the narrative of Thai education. From online platforms to immersive experiences, these tools empower the Strugglers, the very students whose potential remains locked away.

But challenges stand as sentinels guarding this path. Traditional mindsets and tight budgets threaten to stall progress. To forge a new road, collaboration is key. EdTech startups must champion ease of use, affordability, and platform benefits. Financial institutions can bridge the gap with support, knowledge, and affordable financing. The government’s role lies in building robust infrastructure, promoting equitable resource distribution, and incentivizing innovation.

Through the Beacon Impact Fund, Beacon VC aims to propel Thailand towards educational equity, recognizing it as a crucial social pillar within the ESG framework. The fund aims to provide support and network to fast-growing startup companies that aim to excel education equity and democratize access to opportunities across the country.

This journey towards education equity requires not just technology, but a collective will. When EdTech’s tools align with innovation, collaboration, and a focus on the most vulnerable, the Thai educational landscape can blossom into a tapestry of diversity, equity, and inclusion. It is a landscape where every learner, regardless of background, can unlock their full potential and paint their bright future.

 

 

Authors: Woraphot Kingkawkantong (Ping) , Pobtawan Tachachatwanich (Pob)

Editors: Supamas Bunmee (Jae) , Wanwares Boonkong (Pin)

ถอดรหัส CBAM: เส้นทางสู่กรอบแห่งมาตรการปรับราคาคาร์บอนก่อนข้ามพรมแดน

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สหภาพยุโรปได้เริ่มดำเนินการเพื่อก่อให้เกิดการเปลี่ยนแปลงตามวิสัยทัศน์ในการลดการปล่อยก๊าซเรือนกระจกเป็นศูนย์ภายในปี พ.ศ. 2593 หรือใน ค.ศ. 2050 โดยหนึ่งในโครงการริเริ่มที่เป็นที่รู้จักกันดี คือมาตรการการกำหนดราคาคาร์บอนสำหรับบริษัทที่ประกอบธุรกิจในสหภาพยุโรปผ่านระบบการซื้อขายสิทธิในการปล่อยก๊าซเรือนกระจก หรือ EU Emission Trading System (EU ETS) ซึ่งสร้างผลลัพธ์เป็นที่ประจักษ์นับตั้งแต่ปี 2548 มาตรการดังกล่าวเป็นการกำหนดเพดานการปล่อยก๊าซเรือนกระจกสำหรับแต่ละกิจการ โดยผู้ผลิตที่ปล่อยก๊าซเรือนกระจกต่ำกว่าจำนวนที่ระบุไว้สามารถขายสิทธิ์การปล่อยก๊าซเรือนกระจกต่อให้ผู้ผลิตรายอื่นได้ อย่างไรก็ตาม ระบบนี้ส่งผลให้ผู้ผลิตในสหภาพยุโรปเกิดความเสียเปรียบ จนนำมาซึ่งการแข่งขันอย่างไม่เป็นธรรมเมื่อเทียบกับประเทศอื่นที่กฎหมายด้านสิ่งแวดล้อมมีความเข้มงวดน้อยกว่า ทั้งนี้ ในปี 2562 สหภาพยุโรปได้กำหนด European Green Deal เพื่อเร่งดำเนินการลดผลกระทบต่อสภาพภูมิอากาศและสร้างความยั่งยืน โดยแผนการปฏิรูปสีเขียวของสหภาพยุโรปประกอบด้วยกลยุทธ์และมาตรการที่หลากหลาย โดยหนึ่งในนั้นคือมาตรการปรับราคาคาร์บอนก่อนข้ามพรมแดน หรือ Cross-Border Carbon Adjustment Mechanism (CBAM) ซึ่งเป็นมาตรการที่จะช่วยให้สหภาพยุโรปสามารถก้าวสู่เป้าหมายการลดการปล่อยก๊าซเรือนกระจกจนเป็นศูนย์ได้ ขณะเดียวกันก็เป็นการป้องกันไม่ให้เกิดการแข่งขันที่ไม่เป็นธรรมต่อกลุ่มอุตสาหกรรมในสหภาพยุโรปจากค่าใช้จ่ายด้านสิ่งแวดล้อมที่สูงกว่า

CBAM มีความสำคัญต่อภาคธุรกิจ ผู้กำหนดนโยบาย และผู้มีส่วนได้เสียทั่วโลกอย่างยิ่ง มาตรการนี้แสดงให้เห็นถึงความแน่วแน่ของสหภาพยุโรปในการแก้ไขปัญหาการเปลี่ยนแปลงสภาพภูมิอากาศ เราจึงควรศึกษาข้อมูลเกี่ยวกับ CBAM อย่างละเอียด ตั้งแต่ผลกระทบที่จะเกิดขึ้นกับอุตสาหกรรมแต่ละประเภท กรอบระยะเวลาการปรับใช้มาตรการ วิธีการคำนวณการปล่อยมลพิษภายใต้มาตรฐานของ CBAM รวมถึงวิธีการที่ผู้ผลิตสามารถนำมาใช้วัดผล ลด และทำธุรกรรมชดเชยการปล่อยมลพิษจากการประกอบธุรกิจในช่วงแห่งการเปลี่ยนผ่านนี้

อย่างไรก็ตาม CBAM ไม่เพียงสร้างความท้าทายให้กับผู้ที่เกี่ยวข้อง แต่ยังสร้างโอกาสให้กับองค์กรและหน่วยงานหลายภาคส่วน เมื่อผู้ผลิตกำหนดแนวทางการดำเนินธุรกิจเพื่อรับมือการเปลี่ยนผ่านสู่ CBAM สถาบันการเงินก็เป็นหนึ่งในผู้เล่นสำคัญที่จะช่วยเหลือสนับสนุนและให้คำปรึกษาแก่ภาคธุรกิจ การทำงานร่วมกันเช่นนี้จะเร่งผลักดันให้เกิดการเปลี่ยนแปลงไปสู่อนาคตที่สะอาดและยั่งยืนมากขึ้น ในบทความนี้ เราจึงอยากเชิญชวนทุกท่านมาร่วมเดินทางไปถอดรหัส CBAM และทำความเข้าใจเกี่ยวกับเส้นทางสู่ยุคแห่งมาตรการปรับราคาคาร์บอนก่อนข้ามพรมแดนกัน

CBAM คืออะไร

CBAM เป็นหนึ่งในโครงการภายใต้ European Green Deal ซึ่งก่อตั้งขึ้นโดยมีเป้าหมายเพื่อให้สหภาพยุโรปเป็นทวีปแรกที่บรรลุความเป็นกลางทางคาร์บอน (Carbon Neutral) ในปี 2593 ภายหลังจากที่ได้กำหนดเป้าหมายจูงใจนี้ สหภาพยุโรปได้กำหนดหลักเกณฑ์มาตรการภาษีคาร์บอนหลายประการสำหรับผู้ผลิตในสหภาพยุโรป และได้ประกาศมาตรการ CBAM เพื่อส่งเสริมการแข่งขันที่เป็นธรรมและขจัดข้อได้เปรียบด้านราคาของผลิตภัณฑ์นำเข้าจากภูมิภาคที่มาตรการด้านคาร์บอนเข้มงวดน้อยกว่า กล่าวโดยสรุป CBAM เป็นมาตรการการเก็บภาษีคาร์บอนโดยสหภาพยุโรป จากการนำเข้าผลิตภัณฑ์ที่มีการปล่อยคาร์บอนสูงในกระบวนการผลิต โดยมีค่าเทียบเท่ากับภาษีคาร์บอนที่เก็บจากสินค้าประเภทเดียวกันที่ผลิตในสหภาพยุโรปสำหรับการปล่อยก๊าซเรือนกระจกในปริมาณเท่ากัน

ในระยะเริ่มต้น ผู้นำเข้าในสหภาพยุโรปมีหน้าที่เพียงรายงานการปล่อยคาร์บอนของสินค้านำเข้าเท่านั้น อย่างไรก็ตาม ในระยะถัดไปของมาตรการนี้ ผู้นำเข้าจะต้องซื้อ CBAM Certificate เพื่อชดเชยส่วนต่างระหว่างค่าคาร์บอนที่ผู้ผลิตในสหภาพยุโรปต้องชำระกับค่าคาร์บอนที่ผู้ผลิตต่างชาติได้ชำระในประเทศต้นทาง ทั้งนี้ ผู้นำเข้าจะต้องเก็บข้อมูลต่อไปนี้เพื่อการปฏิบัติตามกรอบมาตรการที่จะถูกบังคับใช้

1. ปริมาณสินค้านำเข้าทั้งหมด

2. ค่าคาร์บอนที่ชำระแล้วในประเทศต้นทางสำหรับสินค้าดังกล่าว

3. ปริมาณการปล่อยก๊าซเรือนกระจกทางตรงและทางอ้อมตามจริงของผลิตภัณฑ์นำเข้า

ขอบเขตและกรอบเวลาของ CBAM

ระยะเปลี่ยนผ่านของ CBAM มีผลบังคับใช้ระหว่างเดือนตุลาคม 2566 – ธันวาคม 2568 โดยในระยะนี้ ผู้นำเข้ามีหน้าที่เพียงรายงานข้อมูลที่เกี่ยวข้องกับการนำเข้าดังที่กำหนดข้างต้นเท่านั้น แต่ยังไม่จำเป็นต้องรับรองข้อมูลหรือซื้อ CBAM Certificate ขอบเขตของผลิตภัณฑ์ที่รวมอยู่ในระยะแรกคือธุรกิจที่มีการปล่อยคาร์บอนในปริมาณความเข้มข้นสูง 6 ภาคส่วน ซึ่งมีความเสี่ยงต่อการรั่วไหลของคาร์บอนสูง ได้แก่ ซีเมนต์ อลูมิเนียม ปุ๋ย เหล็กและเหล็กกล้า ไฟฟ้า และไฮโดรเจน

CBAM จะเข้าสู่การบังคับใช้ระยะถาวรในเดือนมกราคม ปี 2569 โดยผู้นำเข้าจะต้องดำเนินการรายงานข้อมูลที่เกี่ยวข้อง นำข้อมูลที่จะนำส่งไปให้ผู้รับรองที่ได้รับอนุญาตรับรองข้อมูล ตลอดจนซื้อ CBAM Certificate สำหรับส่วนต่างค่าคาร์บอนที่ได้ชำระแล้วในประเทศต้นทาง ทั้งนี้ สหภาพยุโรปจะนำข้อมูลที่ได้รับจากช่วงเปลี่ยนผ่านมาทบทวนเพื่อพิจารณาความเป็นไปได้ในการเพิ่มกลุ่มผลิตภัณฑ์ภายใต้ CBAM โดยกลุ่มผลิตภัณฑ์ที่อาจถูกรวมอยู่ในระยะที่สอง ได้แก่ สารเคมีอินทรีย์ พลาสติก และแอมโมเนีย นอกจากนี้ สหภาพยุโรปยังมีแผนที่จะเพิ่มขอบเขตของ CBAM ให้ครอบคลุมกลุ่มผลิตภัณฑ์อื่นๆ ที่สำคัญอีกหลายประเภท โดยมีเป้าหมายจะดำเนินการให้เสร็จสิ้นในปี 2573

 

รายละเอียดการคำนวณการปล่อยก๊าซเรือนกระจกของ CBAM

ขอบเขตการปล่อยก๊าซเรือนกระจกตามแนวปฏิบัติ CBAM จะคล้ายคลึงกับขอบเขตการปล่อยก๊าซเรือนกระจกที่กำหนดโดยมาตรฐานการจัดทำบัญชีและการรายงานก๊าซเรือนกระจกสำหรับองค์กร (GHG Protocol) ซึ่งจะแบ่งขอบเขตการปล่อยก๊าซเรือนกระจกออกเป็น 3 ส่วน ได้แก่

ที่มา: Zevero

  • ขอบเขตที่ 1 หมายถึง การปล่อยก๊าซเรือนกระจกจากการดำเนินงานหรือจากทรัพย์สินของบริษัทเองโดยตรง เช่น การใช้พลังงานฟอสซิลในการผลิตหรือการขนส่ง
  • ขอบเขตที่ 2 หมายถึง การปล่อยก๊าซเรือนกระจกของบริษัททางอ้อมอันเนื่องมาจากกิจกรรมสนับสนุนการดำเนินธุรกิจ เช่น การใช้ไฟฟ้าของเครื่องปรับอากาศหรืออุปกรณ์ให้แสงสว่าง
  • ขอบเขตที่ 3 หมายถึง การปล่อยก๊าซเรือนกระจกทั้งหมดที่บริษัทอาจปล่อยออกมาจาก Value Chain เช่น การให้บริการด้านการเงินแก่ธุรกิจที่ปล่อยก๊าซเรือนกระจก หรือการซื้ออุปกรณ์สำนักงานที่อาจปล่อยก๊าซเรือนกระจกระหว่างกระบวนการผลิต

ที่มา: คณะกรรมาธิการยุโรป

โดยสรุป การปล่อยมลพิษทางตรงตามมาตรการ CBAM คือการปล่อยมลพิษในขอบเขตที่ 1 ตาม GHG Protocol ซึ่งรวมถึงก๊าซเรือนกระจกที่ปล่อยออกมาระหว่างกระบวนการผลิตจากการเผาไหม้ของน้ำมันเชื้อเพลิง หรือการปล่อยมลพิษที่เป็นผลพลอยได้จากปฏิกิริยาทางเคมี หรือกระบวนการสร้างความร้อนและความเย็นที่จำเป็นต่อการผลิต ทั้งนี้ การปล่อยมลพิษอาจคำนวณได้จากการปล่อยมลพิษโดยตรงหรือจากค่าปัจจัยการปล่อยมลพิษ (Emission Factor)

การปล่อยมลพิษทางอ้อมภายใต้ CBAM ครอบคลุมการปล่อยมลพิษขอบเขตที่ 2 และ 3 ตาม GHG Protocol ทั้งนี้ ข้อมูลขอบเขตที่ 2 ที่ผู้นำเข้าจะต้องรายงานตามมาตรการ CBAM จะครอบคลุมการใช้ไฟฟ้าระหว่างกระบวนการผลิตเท่านั้น เช่น การใช้ไฟฟ้าเพื่อก่อให้เกิดแสงสว่าง หรือเครื่องปรับอากาศของโรงงาน เป็นต้น สำหรับขอบเขตที่ 3 ซึ่งเป็นการปล่อยมลพิษขนาดใหญ่ที่สุดและวัดได้ยากที่สุดนั้น CBAM กำหนดให้ผู้นำเข้ารายงานการปล่อยมลพิษจากการผลิตวัตถุดิบต้นทางที่อยู่ภายใต้ขอบเขตการควบคุมของ CBAM เท่านั้น (ซีเมนต์ เหล็ก/เหล็กหล่อ อลูมิเนียม ไฮโดรเจน และปุ๋ย) ในระยะเริ่มต้นนี้ ผู้ผลิตยังไม่ต้องเก็บข้อมูลการปล่อยก๊าซเรือนกระจกจากกิจกรรมที่มีความซับซ้อน เช่น การเดินทางของพนักงาน หรือการใช้ผลิตภัณฑ์ของลูกค้า ทั้งนี้ สามารถดูรายละเอียดเพิ่มเติมเกี่ยวกับการวัดมลพิษสำหรับแต่ละภาคส่วนในแนวปฏิบัติของคณะกรรมาธิการยุโรปได้ ที่นี่

 

CBAM: กำหนดอนาคตด้านการค้าและความยั่งยืน นัยยะต่อเศรษฐกิจประเทศไทย

จากข้อมูลของสำนักงานเศรษฐกิจอุตสาหกรรม ในปี 2565 ประเทศไทยส่งออกเหล็กมูลค่า 201 ล้านเหรียญสหรัฐฯ และอลูมิเนียม 111 ล้านเหรียญสหรัฐฯ ไปยังประเทศต่าง ๆ ในสหภาพยุโรป ถึงแม้ว่ามูลค่าการส่งออกนี้จะคิดเป็นเพียง 1.3% ของมูลค่าการส่งออกทั้งหมดในปี 2565 ก็ตาม แต่ขอบเขตของ CBAM ที่ขยายออกมาครอบคลุมกลุ่มอุตสาหกรรมอื่น ๆ เช่น พลาสติก จะส่งผลกระทบต่อเศรษฐกิจมากขึ้น เช่น การส่งออกพลาสติกซึ่งมีมูลค่า 676 พันล้านเหรียญสหรัฐฯ หรือคิดเป็น 2.4% ของมูลค่าการส่งออกทั้งหมด

ผู้ส่งออกกลุ่มผลิตภัณฑ์ข้างต้นทุกรายมีหน้าที่รายงานการปล่อยมลพิษต่อสหภาพยุโรป ยกเว้นผลิตภัณฑ์ที่มีมูลค่าต่ำกว่า 150 ยูโร หรือผลิตภัณฑ์สำหรับใช้งานในกองทัพ ทั้งนี้ หลังจากระยะเปลี่ยนผ่าน ผู้ส่งออกชาวไทยจะต้องปฏิบัติตามกระบวนการที่เพิ่มขึ้น ทั้งการส่งรายงานให้กับผู้รับรองที่ได้รับอนุญาตเพื่อตรวจสอบความถูกต้อง และการชำระค่าภาษีคาร์บอนสุทธิจากจำนวนที่ได้จ่ายไปในประเทศต้นทาง

ความเสียเปรียบด้านเศรษฐกิจของผู้ส่งออกชาวไทย

จากข้อมูลของ Statista ราคาคาร์บอนในระบบ EU ETS ที่จะถูกนำมาใช้เป็นราคาคาร์บอนอ้างอิงในระยะเริ่มต้นนั้นมีมูลค่าผันแปรระหว่าง 80-100   ยูโรต่อตันคาร์บอนไดออกไซด์ในช่วงครึ่งปีแรกของปี 2566 โดยคาดการณ์ว่าราคาจะเพิ่มสูงขึ้นเมื่อกลไก CBAM ถูกนำมาใช้อย่างเต็มระบบจากความต้องการซื้อสิทธิ์การปล่อยก๊าซเรือนกระจกที่เพิ่มขึ้น ในช่วงที่สิทธิ์การปล่อยแบบไม่มีค่าใช้จ่ายในระบบ EU ETS ค่อยๆ ถูกปรับลดลง เพื่อเปลี่ยนผ่านเข้าสู่ CBAM อย่างสมบูรณ์ โดยวิธีการหนึ่งที่แสดงให้เห็นถึงข้อเสียเปรียบทางเศรษฐกิจของผู้ส่งออกชาวไทยคือการคำนวณความแตกต่างของค่าใช้จ่ายในการปล่อยมลพิษต่อตันของผลิตภัณฑ์เทียบระหว่างประเทศไทยและผู้ส่งออกที่เป็นคู่แข่งรายอื่น ซึ่งสามารถดูตัวอย่างการเปรียบเทียบภาษีคาร์บอนของผลิตภัณฑ์เหล็กและอลูมิเนียมปฐมภูมิได้ในตารางต่อไปนี้

  เหล็ก: ประเทศไทย เหล็ก: ทั่วโลก เหล็ก: สหภาพยุโรป อลูมิเนียมปฐมภูมิ: ประเทศไทย อลูมิเนียมปฐมภูมิ: ทั่วโลก อลูมิเนียมปฐมภูมิ: สหภาพยุโรป
1ค่าคาร์บอน (เหรียญสหรัฐฯ/ตันคาร์บอนไดออกไซด์เทียบเท่า) 96.3 96.3 96.3 96.3 96.3 96.3
2การปล่อย (ตันคาร์บอนไดออกไซด์เทียบเท่า/ตัน) 1.55 1.40 1.14 12.24 12.50 6.20
ภาษีคาร์บอน (1*2) (เหรียญสหรัฐฯ) 149.48 134.82 109.78 1,178.32 1,203.75 597.06
เปรียบเทียบกับประเทศไทย (%) -10% -27% 2% -49%

เมื่อพิจารณาข้อมูลในตารางข้างต้น จะเห็นได้ว่าผู้ส่งออกชาวไทยมีความเสียเปรียบเนื่องจากมีภาระค่าภาษีคาร์บอนที่สูงกว่า และความเสียเปรียบนี้ยิ่งเห็นได้ชัดเมื่อเทียบกับผู้ผลิตในสหภาพยุโรป ในท้ายที่สุด ค่าใช้จ่ายที่เพิ่มขึ้นนี้จะทำให้ผู้ซื้อชาวยุโรปต้องซื้อสินค้าในราคาแพงขึ้นหรือผู้ส่งออกจะมีกำไรที่ลดลงจากการรับภาระค่าใช้จ่ายที่เพิ่มขึ้นนี้ไว้เอง ผลกระทบนี้มีแนวโน้มจะเปลี่ยนแปลงการส่งออกสินค้าตาม CBAM ของไทยไปยังประเทศอื่นนอกภูมิภาคยุโรปในระยะสั้นถึงระยะกลางหากมีผู้ซื้อสินค้ารายอื่น อย่างไรก็ตาม ผู้ผลิตจำเป็นต้องค่อยๆ พัฒนาเทคโนโลยีการผลิตของตนให้ดีขึ้น รวมถึงพิจารณาเปลี่ยนไปใช้เทคโนโลยีการผลิตที่เป็นมิตรต่อสิ่งแวดล้อมมากขึ้น เนื่องจากอีกไม่นานประเทศอื่นๆ เช่น สหรัฐอเมริกา ก็จะบังคับใช้กลไกเช่นเดียวกันนี้เพื่อลดการปล่อยก๊าซเรือนกระจก

ความก้าวหน้าในปัจจุบันของหน่วยงานภาครัฐ

เพื่อตอบสนองต่อการเปลี่ยนแปลงอย่างมีนัยสำคัญครั้งนี้ กรมเจรจาการค้าระหว่างประเทศ สภาอุตสาหกรรมแห่งประเทศไทย และองค์การบริหารจัดการก๊าซเรือนกระจก (องค์การมหาชน) ได้ร่วมมือกันจัดสัมมนาเกี่ยวกับมาตรการ CBAM โดยมีจุดประสงค์เพื่อให้ความรู้แก่ผู้มีส่วนได้ส่วนเสียและรวบรวมข้อเสนอแนะเกี่ยวกับข้อวิตกกังวลในระยะเริ่มต้นของการบังคับใช้ CBAM ผู้ผลิตชาวไทยได้ขอรับความช่วยเหลือเป็นกรณีพิเศษเกี่ยวกับเทคโนโลยีการรายงาน การอนุญาตให้ใช้ผู้รับรองรายงานชาวไทยเพื่อลดต้นทุน และผ่อนปรนการลงโทษในกรณีที่เกิดข้อผิดพลาดในการรายงานโดยไม่ได้เจตนาในระยะปรับตัว ทั้งนี้ ในปัจจุบัน กรมเจรจาการค้าระหว่างประเทศและสภาอุตสาหกรรมแห่งประเทศไทยได้ร่วมหารือกับผู้แทนสหภาพยุโรปเพื่อหาทางออกที่เป็นไปได้ในการช่วยบรรเทาผลกระทบต่ออุตสาหกรรมไทย โดยเราคาดว่าจะทราบข้อมูลเพิ่มเติมเกี่ยวกับผลการเจรจาในอนาคตอันใกล้นี้

เมื่อต้นปีที่ผ่านมา องค์การบริหารจัดการก๊าซเรือนกระจกได้ร่วมมือกับกระทรวงการอุดมศึกษา วิทยาศาสตร์ วิจัยและนวัตกรรม และมหาวิทยาลัยชั้นนำ 5 แห่ง เช่น จุฬาลงกรณ์มหาวิทยาลัย และมหาวิทยาลัยธรรมศาสตร์ คิดค้นหลักสูตรใหม่เพื่อสร้างอาชีพด้านความยั่งยืน โดยเน้นส่งเสริมความรู้เฉพาะทางด้านการจัดการการปล่อยคาร์บอน และการใช้งานคาร์บอนเครดิต โครงการทางด้านการศึกษามีเป้าหมายเพื่อสนับสนุนให้การเปลี่ยนผ่านเข้าสู่ CBAM ของภาคธุรกิจเป็นไปอย่างราบรื่น

นอกเหนือจากโครงการทางด้านการศึกษาแล้ว องค์การบริหารจัดการก๊าซเรือนกระจกยังอยู่ระหว่างการพัฒนาระบบสำหรับการคำนวณการปล่อยก๊าซเรือนกระจก โดยระบบดังกล่าวจะกลายเป็นเครื่องมือที่สำคัญสำหรับผู้ผลิตชาวไทยในการรายงานการปล่อยคาร์บอนตามกฎเกณฑ์ของ CBAM โดยปัจจุบัน การพัฒนาระบบอยู่ในขั้นของการทำ Pilot Testing โดยมีอาสาสมัครเข้าร่วมทดสอบหลายบริษัทด้วยกัน เมื่อการพัฒนาเสร็จสิ้น โครงการนี้จะช่วยลดต้นทุนของผู้ส่งออกไทยในการรายงานได้อย่างมีนัยยะสำคัญ

 

มุ่งหน้าสู่การเปลี่ยนผ่าน: ความท้าทายและโอกาสสำหรับผู้ผลิตและสตาร์ทอัพ

เพื่อคงไว้ซึ่งความสามารถในการแข่งขันในระยะยาว ผู้ผลิตต้องปรับเปลี่ยนกิจกรรมหลัก 3 ประการ ได้แก่ การวัดปริมาณการปล่อยก๊าซเรือนกระจกอย่างถูกต้อง การลดการปล่อยก๊าซเรือนกระจกอย่างมีประสิทธิภาพ และการดำเนินการเพื่อชดเชยการปล่อยคาร์บอน ซึ่งกิจกรรมเหล่านี้แม้จะมีความท้าทายในตัวเอง แต่ก็เอื้อประโยชน์ให้สตาร์ทอัพได้พลิกวิกฤติเป็นโอกาสด้วยการนำเสนอวิธีการแก้ไขปัญหาได้

1. การวัดปริมาณการปล่อยก๊าซเรือนกระจกอย่างถูกต้อง

การวัดปริมาณการปล่อยมลพิษอย่างถูกต้องเปรียบเสมือนรากฐานของ CBAM และการลดการปล่อยก๊าซเรือนกระจก ซึ่งสอดคล้องกับปรัชญาไร้กาลเวลาของปีเตอร์ ดรัคเกอร์ (Peter Drucker) ที่กล่าวไว้ว่า “คุณไม่สามารถบริหารจัดการสิ่งที่วัดค่าไม่ได้” ทั้งนี้  สตาร์ทอัพทั่วโลกกำลังแก้ไขปัญหานี้ด้วยการคิดค้นนวัตกรรมในการวัดค่าคาร์บอนด้วย Carbon Accounting System ผู้ที่มีบทบาทในการจัดทำ Carbon Accounting System ที่มีชื่อเสียงในภูมิภาค เช่น Terrascope, RIMM, Unravel Carbon ผู้ให้บริการเหล่านี้ได้เริ่มพัฒนาระบบเพื่อแก้ไขปัญหาความท้าทายนี้ อย่างไรก็ตาม การแก้ปัญหานี้มีความซับซ้อน เนื่องจากมีหลายแง่มุมที่ต้องคำนึงถึง ทั้งนี้ เพื่อให้เห็นภาพความท้าทายที่ชัดเจนมากขึ้น เราจะไปดูอุปสรรคที่ผู้ประกอบการกำลังเผชิญในปัจจุบันกัน

1.1 ปัญหาเรื่องความไม่ถูกต้องของข้อมูล ซึ่งส่วนมากเกิดจากความท้าทายในการวัดปริมาณการปล่อยก๊าซเรือนกระจกตามขอบเขตที่ 3 อย่างถูกต้อง และความท้าทายในเรื่องมาตรฐานของข้อมูลที่มาจากหลายแหล่งที่แตกต่างกัน เช่น เครื่องมือ อุปกรณ์ ผู้ผลิตที่แตกต่างกัน อย่างไรก็ตาม การรวมฟังก์ชันเหล่านี้เข้าไว้ใน Carbon Accounting System จะช่วยปรับปรุงความถูกต้องและลดปัญหาข้อมูลไม่เป็นมาตรฐานได้

1.1.1 การคำนวณคาร์บอนโดยใช้ Emission Factor (EF) – ปลดล็อคศักยภาพด้วยระดับความละเอียดของข้อมูล ปัจจัยสำคัญพื้นฐานของการวัดปริมาณการปล่อยก๊าซเรือนกระจกอย่างถูกต้องคือความละเอียดและความพร้อมใช้ของข้อมูล  Emission Factor เป็นค่าเฉพาะอุตสาหกรรมที่สามารถนำมาใช้ในการคำนวณปริมาณการปล่อยคาร์บอนได้ เช่น จำนวนวัตถุดิบที่ใช้ในการผลิต หรือกรรมวิธีการผลิตที่ผู้ผลิตเลือกใช้

1.1.2 การบูรณาการตลอด Supply Chainการเชื่อมโยงผู้เล่นในระบบนิเวศเพื่อให้บรรลุวัตถุประสงค์ในการวัดปริมาณการปล่อยก๊าซเรือนกระจกได้อย่างถูกต้อง ผู้ผลิตต้องติดตามวงจรชีวิตของผลิตภัณฑ์ไปจนหมดอายุการใช้งาน โดยขยายขอบเขตออกไปนอกเหนือจากโรงงานของผู้ผลิตเองเพื่อให้ครอบคลุมไปถึงผู้ขายด้วย การบูรณาการข้อมูลใน Supply Chain นั้นเป็นสิ่งที่สำคัญ เพราะหากระบบสามารถรวบรวมข้อมูลการปล่อยก๊าซเรือนกระจกจากผู้ขายได้ทุกราย และมีข้อมูลครบถ้วน ระบบจะช่วยให้การวัดปริมาณการปล่อยก๊าซเรือนกระจกตามขอบเขตที่ 3 ถูกต้องแม่นยำมากขึ้น ทำให้ผู้ผลิตเห็นภาพองค์รวม Carbon Footprint ของผลิตภัณฑ์

1.1.3 การจัดเก็บข้อมูลคาร์บอนโดยใช้เทคโนโลยี Blockchainเพิ่มความถูกต้องของข้อมูล ความถูกต้องของข้อมูลเป็นสิ่งสำคัญที่สุดในการวัดปริมาณการปล่อยก๊าซเรือนกระจก ความไม่ถูกต้องมักจะเกิดจากการขาดความเชื่อมั่นและความโปร่งใส การใช้เทคโนโลยี Blockchain เพื่อเก็บข้อมูลคาร์บอนจะช่วยยกระดับความถูกต้องและความโปร่งใสของข้อมูลได้ การจัดเก็บข้อมูลคาร์บอนโดยใช้ Blockchain จะบันทึกข้อมูลพร้อมกับเวลา แล้วเชื่อมโยงข้อมูลธุรกรรมเกี่ยวกับการปล่อยคาร์บอนไปยัง Decentralized Network การดำเนินการเช่นนี้ไม่เพียงแต่ช่วยให้ข้อมูลที่รายงานมีความน่าเชื่อถือมากขึ้น แต่ยังทำให้ผู้มีส่วนได้เสียสามารถติดตามแหล่งที่มาของการปล่อยคาร์บอนได้อีกด้วย

1.2 ปัญหาการใช้แรงงานจำนวนมาก การวัดปริมาณการปล่อยคาร์บอนในปัจจุบันเป็นขั้นตอนที่ต้องใช้แรงงานจำนวนมาก ส่งผลให้มีค่าใช้จ่ายสูงและมีแนวโน้มจะเกิดความผิดพลาดได้ง่าย อย่างไรก็ตาม การใช้ระบบอัตโนมัติและเทคโนโลยีจะเข้ามาช่วยแก้ไขปัญหานี้ได้

1.2.1 การบูรณาการระบบและอุปกรณ์ศักยภาพของระบบอัตโนมัติ หนึ่งในความท้าทายของการวัดปริมาณการปล่อยคาร์บอนในปัจจุบันคือเป็นกระบวนการที่ใช้แรงงานจำนวนมาก ซึ่งมักจะทำให้มีค่าใช้จ่ายสูงและมีแนวโน้มจะเกิดความผิดพลาด ด้วยเหตุนี้ การเชื่อมโยง Carbon Accounting System เข้ากับเครื่องมือ เช่น IoT (เครื่องมือที่เชื่อมต่อและแบ่งปันข้อมูลผ่านอินเตอร์เน็ต) ERP (ระบบบริหารจัดการทรัพยากรภายในองค์กร) หรือเครื่องจักรจะทำให้ธุรกิจสามารถปลดล็อคศักยภาพในการดึงข้อมูลตามกิจกรรมที่ดำเนินการได้โดยอัตโนมัติ ซึ่งเป็นการต่อยอดสู่ธุรกิจที่ขับเคลื่อนด้วยระบบอัตโนมัติที่จะช่วยลดการใช้แรงงานคน ส่งผลให้กระบวนการมีประสิทธิภาพและคุ้มค่ามากยิ่งขึ้น

1.2.2 Optical Character Recognition (OCR)จัดระเบียบทั้งเอกสารที่เป็นกระดาษและไฟล์ดิจิทัลได้อย่างราบรื่น การเก็บเอกสารที่เป็นกระดาษถือเป็นความท้าทายของการวัดปริมาณการปล่อยก๊าซเรือนกระจกที่มีมาอย่างยาวนาน เนื่องจากจำเป็นต้องใช้คนในการป้อนข้อมูล อย่างไรก็ตาม OCR จะช่วยแก้ไขปัญหานี้ได้ ด้วยการเปลี่ยนตัวอักษรบนเอกสารรูปแบบกระดาษให้เป็นรูปแบบดิจิทัล วิธีนี้จะนำไปสู่กระบวนการคำนวณการปล่อยมลพิษที่มีประสิทธิภาพและสามารถดำเนินการได้โดยอัตโนมัติ การแปลงเอกสารเหล่านี้ให้เป็นรูปแบบดิจิทัลจะทำให้กระบวนการทั้งหมดรวดเร็ว ถูกต้อง และคุ้มค่าต้นทุนมากขึ้น

2. การลดปริมาณการปล่อยก๊าซเรือนกระจกอย่างมีประสิทธิภาพและยั่งยืน

เมื่อวัดปริมาณการปล่อยมลพิษได้อย่างถูกต้องแล้ว ผู้ผลิตจะต้องลดมลพิษในระหว่างกระบวนการผลิตเพื่อลดการจ่ายภาษีคาร์บอนด้วย อย่างไรก็ตาม ขั้นตอนนี้ยังมีอุปสรรคในการดำเนินงานต่าง ๆ ได้แก่ ข้อจำกัดด้านเทคนิค และข้อจำกัดทางเศรษฐกิจ

2.1 ข้อจำกัดด้านเทคนิค การแก้ไขปัญหาข้อจำกัดด้านเทคนิคเพื่อลดมลพิษนั้นจำเป็นต้องใช้กลยุทธ์หลายด้าน ผู้ผลิตต้องเผชิญกับความท้าทายสำคัญ 3 ประการ ได้แก่ การค้นหาวัสดุทางเลือกที่คงทนและเป็นมิตรต่อสิ่งแวดล้อม อุตสาหกรรมที่ใช้พลังงานอย่างมากจำเป็นต้องหาแหล่งจ่ายพลังงานหมุนเวียน และจำเป็นต้องลดการปล่อยมลพิษในกระบวนการผลิตที่ปล่อยมลพิษสูง เพื่อตอบสนองต่อความท้าทายนี้ หลายหน่วยงานมีการวิจัยและศึกษาเพื่อค้นหาทางออกที่ขับเคลื่อนด้วยนวัตกรรมเพื่อลดการปล่อยมลพิษในหลาย ๆ ด้าน

2.2.1 วัสดุจากนวัตกรรม เพื่อการใช้วัสดุที่ยั่งยืนกว่า ปัจจุบันผู้ผลิตมีทางเลือกที่มากขึ้น เช่น โครงการริเริ่มเพื่อพัฒนา Inert anode สำหรับการหลอมอลูมิเนียมของ ELYSIS หรือการนำวัสดุกลับมาใช้ใหม่ของ HARBOR Aluminum วัสดุที่ผลิตจากนวัตกรรมเหล่านี้มีจุดประสงค์เพื่อลดการปล่อยมลพิษและพัฒนาอุตสาหกรรมให้ยั่งยืน

2.2.2 การลดก๊าซเรือนกระจกป้องกันการปล่อยมลพิษที่แหล่งกำเนิด อีกวิธีการหนึ่งที่มีการคิดค้นขึ้น คือการพัฒนาเทคโนโลยีที่ช่วยลดการปล่อยก๊าซเรือนกระจกระหว่างกระบวนการผลิต โครงการริเริ่มต่างๆ เช่น การดำเนินการของ Analytics Shop เพื่อผลิตตัวยับยั้งการปลดปล่อยไนโตรเจน (Nitrification Inhibitors) ในปุ๋ย และความพยายามของ Hybrit ในการใช้ไฮโดรเจนสำหรับกระบวนการผลิตสินแร่เหล็กแทนการใช้ก๊าซคาร์บอนไดออกไซด์ จะช่วยลดก๊าซเรือนกระจกได้

2.2.3 การบริหารจัดการสิ่งอำนวยความสะดวกให้มีประสิทธิภาพมากขึ้นการดำเนินงานอัจฉริยะ บริษัทอย่าง AltoTech, TIE-Smart และ Zenatix มีการให้บริการเทคโนโลยีอาคารอัจฉริยะเพื่อควบคุมการบริหารจัดการการใช้พลังงานภายในอาคารให้เหมาะสมและลดการปล่อยมลพิษ ส่งผลให้การดำเนินงานเป็นไปอย่างยั่งยืน

2.2.4 การใช้พลังงานหมุนเวียนทดแทนพลังงานขาดแคลน เราจำเป็นที่จะต้องเร่งแก้ไขปัญหาการขาดแคลนพลังงานหมุนเวียน โดยเฉพาะสำหรับอุตสาหกรรมที่ต้องใช้พลังงานจำนวนมาก จากข้อมูลของสำนักงานนโยบายและแผนทรัพยากรธรรมชาติและสิ่งแวดล้อม ประเทศไทย พบว่า ในปี 2564 ประเทศไทยใช้พลังงานจากแหล่งพลังงานหมุนเวียนเพียง 11% เท่านั้น ซึ่งยังมีค่าต่ำกว่าของสหภาพยุโรปที่มีการใช้พลังงานหมุนเวียนเกือบ 40% ดังนั้น ประเทศไทยยังสามารถปรับปรุงการใช้พลังงานได้อีกมาก ทั้งนี้ บริษัทต่าง ๆ ที่มีการให้บริการพลังงานหมุนเวียน เช่น Clover Power และ First Korat Wind จะเป็นส่วนสำคัญที่ช่วยเติมเต็มการขาดแคลนพลังงานหมุนเวียนได้

2.2.5 เทคโนโลยีการดักจับก๊าซเรือนกระจก การดักจับมลพิษ โรงงานผลิตเป็นแหล่งปล่อยก๊าซเรือนกระจกปริมาณมาก โดยเฉพาะอย่างยิ่งก๊าซคาร์บอนไดออกไซด์ ซึ่งก่อให้เกิดภาวะโลกร้อน ดังนั้น การดักจับการปล่อยมลพิษเหล่านี้ที่แหล่งกำเนิดจะทำให้เราสามารถป้องกันไม่ให้มลพิษถูกปล่อยออกสู่บรรยากาศจนก่อให้เกิดภาวะเรือนกระจกได้ ทั้งนี้ เทคโนโลยีต่าง ๆ เช่น  Carbon Capture, Usage, and Storage (CCUS) โดย Technip และ Linde ควบคู่กับเทคโนโลยี Direct Air Capture (DAC) ที่พัฒนาโดยบริษัทต่าง ๆ เช่น Carbon Engineering และ Climeworks ล้วนมีบทบาทสำคัญในการดักจับและลดการปล่อยมลพิษที่แหล่งกำเนิดโดยตรง

2.2 ข้อจำกัดทางเศรษฐกิจ ข้อจำกัดทางเศรษฐกิจมักมาพร้อมการปรับใช้เทคโนโลยีใหม่ที่มีความสะอาดมากขึ้น ข้อจำกัดนี้ถือเป็นความท้าทายสำคัญสำหรับธุรกิจที่พยายามลด Carbon Footprint ตัวอย่างเช่น ต้นทุนการลงทุนแรกเริ่มที่มีมูลค่าสูง ต้นทุนการพัฒนาทักษะของแรงงาน และต้นทุนค่าเสียโอกาสจากช่วงที่โรงงานต้องหยุดทำงาน อย่างไรก็ตาม ผู้ผลิตมีทางเลือกหลากหลายในการแก้ไขปัญหาที่เกิดจากข้อจำกัดทางเศรษฐกิจเหล่านี้ เพื่อมุ่งสู่ความยั่งยืน

2.2.1 Sustainable Finance – การเข้าถึงแหล่งเงินทุนในช่วงเปลี่ยนผ่าน กลยุทธ์หนึ่งที่สำคัญคือการใช้ทางเลือก Sustainable Finance ผู้ให้บริการต่าง ๆ เช่น GoParity และ BluePath Finance มีบริการที่ช่วยอำนวยความสะดวกในการเข้าถึงเงินทุนที่จำเป็นเพื่อลงทุนในเทคโนโลยีสะอาด ทั้งนี้ การดำเนินการเช่นนี้ไม่เพียงแต่จะช่วยแก้ไขปัญหาการขาดแคลนเงินทุนในช่วงเริ่มต้นเท่านั้น แต่ยังช่วยให้บริษัทประสบความสำเร็จในการเปลี่ยนผ่านสู่ความยั่งยืนในวงกว้างด้วย

2.2.2 การวิเคราะห์ข้อมูลและเครื่องมือเพื่อปรับค่าการปล่อยมลพิษอย่างเหมาะสมเพิ่มผลตอบแทนสูงสุดจากการลงทุนเพื่อลดผลกระทบ ในโลกธุรกิจที่มีข้อจำกัดด้านงบประมาณ บริษัทต้องทำการตัดสินใจเชิงกลยุทธ์ว่าจะลงทุนเพื่อลดมลพิษที่ส่วนใด ดังนั้น การวิเคราะห์ข้อมูลและการใช้เครื่องมือเฉพาะจึงมีบทบาทสำคัญมากในการช่วยระบุว่าการลงทุนในจุดใดของกระบวนการผลิตจะช่วยลดการปล่อยคาร์บอนได้มากที่สุด การดำเนินการเช่นนี้จะทำให้เจ้าของธุรกิจจัดลำดับความสำคัญในการใช้งบประมาณได้อย่างมีประสิทธิภาพ และทำการตัดสินใจด้วยข้อมูลว่าจุดใดคือเป้าหมายที่จะสร้างผลกระทบสูงสุดในการลด Carbon Footprint

2.2.3 การวิเคราะห์ Supply Chain – การเชื่อมโยงความสัมพันธ์ การวิเคราะห์ Supply Chain มีบทบาทสำคัญในการบริหารจัดการข้อจำกัดด้านเศรษฐกิจเพื่อลด Carbon Footprint ผู้ผลิตสามารถค้นหาผู้จัดจำหน่ายวัตถุดิบที่มีการปล่อยมลพิษปริมาณต่ำภายใต้ขอบเขตของ CBAM ได้ โดยมีบริษัทผู้ให้บริการ Carbon Accounting System ต่าง ๆ เช่น Pantas และ Terrascope ที่มีบริการด้านการวิเคราะห์การปล่อยคาร์บอนในห่วงโซ่อุปทาน ที่สามารถให้ข้อมูลแก่ผู้ผลิตเพื่อนำไปประกอบการตัดสินใจจัดหาแหล่งวัตถุดิบจากผู้จัดจำหน่ายที่ดำเนินการโดยมีความรับผิดชอบต่อสิ่งแวดล้อม

3. การดำเนินการเพื่อชดเชยการปล่อยคาร์บอน

เมื่อผู้ผลิตได้ดำเนินการเพื่อลดการปล่อยคาร์บอนแล้ว ขั้นตอนสำคัญลำดับที่สามคือการจัดการการปล่อยมลพิษที่ยังคงเหลือไม่ว่าจะด้วยการจ่ายภาษีคาร์บอนหรือการซื้อคาร์บอนเครดิต ถึงแม้ว่าทั้งสองทางเลือกนั้นจำเป็นต้องใช้เงินลงทุน การจ่ายค่าคาร์บอนให้กับองค์กรที่สนับสนุนโครงการเพื่อสิ่งแวดล้อมภายในประเทศของตนย่อมเป็นทางเลือกที่ดีกว่า อย่างไรก็ตาม การดำเนินการเพื่อชดเชยการปล่อยคาร์บอนมีความซับซ้อนที่จำเป็นต้องใช้ความรู้เฉพาะทางเพื่อให้บรรลุเป้าหมายได้อย่างมีประสิทธิภาพ นอกจากนี้ สหภาพยุโรปยังไม่ได้ประกาศกฎเกณฑ์ที่ชัดเจนเกี่ยวกับการซื้อคาร์บอนเครดิตเพื่อชดเชยการปล่อยคาร์บอนภายใต้ CBAM เราจึงจำเป็นต้องติดตามกฎระเบียบที่จะประกาศเพิ่มเติมในไตรมาสที่สองปี 2568 อย่างใกล้ชิด

หากกฎเกณฑ์ของ CBAM อนุญาตให้ทำได้ ขอบเขตของการชดเชยการปล่อยคาร์บอนด้วยการซื้อคาร์บอนเครดิตจะเป็นไปตามระเบียบที่กำหนดขึ้นโดยสหภาพยุโรป โดยผู้ผลิตจะสามารถนำคาร์บอนเครดิตที่ซื้อมาหักออกจากค่าภาษีคาร์บอนที่ต้องจ่ายภายใต้ CBAM ในภาพรวมได้ จำนวนคาร์บอนเครดิตที่อนุญาตให้ซื้อได้ตามจริงจะขึ้นอยู่กับระเบียบของ CBAM และอาจแตกต่างกันไปโดยขึ้นอยู่กับปัจจัยต่าง ๆ เช่น ประเภทของอุตสาหกรรม และประวัติรายงานการปล่อยมลพิษ

ผู้ให้บริการ Carbon Credit Exchange เช่น T-VER และ Climate Impact X รวมถึง Renewable Energy Credit Exchange เช่น Innopower มักจะมีแนวปฏิบัติและการอบรมให้ความรู้เพื่อสนับสนุนผู้ผลิตสำหรับการซื้อขายคาร์บอนเครดิต นอกจากนี้ ผู้ผลิตยังสามารถติดตามกฎเกณฑ์ใหม่ๆ ของ CBAM ได้ผ่านทางเว็บไซต์ของ CBAM เอง แหล่งข้อมูลเหล่านี้จะช่วยให้ธุรกิจสามารถดำเนินการเพื่อชดเชยคาร์บอนได้อย่างราบรื่นและสอดคล้องกับข้อกำหนดของ CBAM เพื่อบรรลุเป้าหมายความยั่งยืนได้

บทบาทของสถาบันการเงินเพื่อสนับสนุนการเปลี่ยนผ่านสู่ CBAM อย่างราบรื่น

เมื่อผู้ผลิตเริ่มดำเนินการเพื่อลดการปล่อยคาร์บอนและปรับเปลี่ยนกระบวนการให้สอดคล้องกับข้อกำหนดของ CBAM ผู้ผลิตจะเผชิญกับความท้าทายในหลากหลายรูปแบบ นับตั้งแต่การวัดปริมาณการปล่อยก๊าซเรือนกระจกไปจนถึงการใช้เทคโนโลยีที่มีนวัตกรรมเพื่อลดการปล่อยมลพิษ จึงถึงขั้นตอนสุดท้ายคือการก้าวข้ามผ่านกระบวนการชดเชยคาร์บอนที่มีความซับซ้อน โดยในแต่ละขั้นตอนมีอุปสรรคเฉพาะที่ผู้ผลิตต้องเผชิญแตกต่างกันไป

อย่างไรก็ตาม หัวใจสำคัญของความท้าทายเหล่านี้มีจุดร่วมที่คล้ายกันคือ ความจำเป็นในการเข้าถึงแหล่งเงินทุนเพื่อสนับสนุนการพัฒนาและการปรับใช้เทคโนโลยีเพื่อรักษาสภาพภูมิอากาศอย่างยั่งยืน ในระยะเริ่มต้นของการวัดและลดการปล่อยมลพิษ ผู้ผลิตมักจะเผชิญกับอุปสรรคด้านการเงินเพื่อลงทุนในเครื่องมือ กระบวนการ และโครงสร้างพื้นฐานใหม่ โดยข้อจำกัดด้านการเงินนี้อาจเป็นอุปสรรคสำคัญต่อความก้าวหน้าในการดำเนินงานได้

เมื่อเรามองในอีกแง่มุมหนึ่ง ผู้ผลิตที่ต้องทำการชดเชยการปล่อยคาร์บอนจะเผชิญกับความท้าทายในเรื่องของความรู้ความเข้าใจจากความซับซ้อนของการซื้อขายคาร์บอนเครดิต ความเข้าใจระเบียบของ CBAM และการบริหารจัดการกลยุทธ์การชดเชยการปล่อยมลพิษได้อย่างมีประสิทธิภาพ ซึ่งอาจจะทำให้ผู้ผลิตรู้สึกกังวลหากไม่มีความเชี่ยวชาญ

ความท้าทายเหล่านี้เปิดโอกาสให้สถาบันการเงินเข้ามามีบทบาทสำคัญในการช่วยให้ผู้ผลิตเปลี่ยนผ่านไปสู่ CBAM ได้อย่างราบรื่น โดยสถาบันการเงินถือเป็นหน่วยงานที่อยู่ในสถานะที่สามารถช่วยแก้ไขปัญหาความท้าทายสำคัญทั้งสองประการได้ ดังนี้

1. การวัด 2. การลด 3. การทำธุรกรรม
 คำนิยาม การวัดคือกระบวนการประเมินและระบุปริมาณของก๊าซเรือนกระจกที่ปล่อยออกสู่บรรยากาศจากกิจกรรมของผู้ผลิต การลด หมายถึง การดำเนินการลดปริมาณการปล่อยก๊าซเรือนกระจกจากกระบวนการของผู้ผลิต การทำธุรกรรม หมายถึงกระบวนการซื้อหรือขายคาร์บอนเครดิตเพื่อชดเชยการปล่อยก๊าซเรือนกระจก
 ประเด็น 1.1 ขาดข้อมูลที่ถูกต้อง

1.2 การใช้แรงงานจำนวนมาก

2.1 ข้อจำกัดด้านเทคนิค

2.2 ข้อจำกัดทางการเงิน

3.1 ขาดความรู้/ความเชี่ยวชาญ

3.2 ความซับซ้อนของระเบียบ

 บทบาทของ   สถาบันการ   เงิน

 

 

 

 

 

 

ช่วยเร่งการพัฒนาเทคโนโลยีและการปรับใช้เทคโนโลยีให้แพร่หลายโดยสนับสนุนการเข้าถึงแหล่งเงินทุน

1.1 ผู้ให้บริการเงินกู้เพื่อสิ่งแวดล้อมที่มีดอกเบี้ยต่ำ

1.2 ผู้ลงทุนในสตาร์ทอัพด้านเทคโนโลยีเพื่อสภาพภูมิอากาศ

1.3 ผู้จัดการกองทุนเพื่อความยั่งยืน

1.4 พันธมิตรในการให้บริการเทคโนโลยีเพื่อสภาพภูมิอากาศ

สร้างเสริมทักษะความรู้ให้ผู้ประกอบการด้วยทรัพยากรขององค์กร

2.1 ผู้สอนและให้ความรู้

2.2 ผู้ให้คำปรึกษาด้านความยั่งยืนแก่ลูกค้า

2.3 ศูนย์รวมทรัพยากรทางเทคโนโลยีเพื่อสภาพภูมิอากาศที่น่าเชื่อถือ

การแก้ไขความท้าทายด้านเงินทุนสำหรับเทคโนโลยีเพื่อสภาพภูมิอากาศ

1. ผู้ให้บริการเงินกู้เพื่อสิ่งแวดล้อมที่มีดอกเบี้ยต่ำ: สถาบันการเงินสามารถยื่นมือเข้าช่วยได้ด้วยการเสนอเงินกู้เพื่อสิ่งแวดล้อมดอกเบี้ยต่ำให้กับทั้งลูกค้ารายย่อยและบริษัทที่มองหาเงินทุนเพื่อพัฒนาและนำเทคโนโลยีที่เป็นมิตรต่อสภาพภูมิอากาศมาใช้งาน การกำหนดหลักเกณฑ์เกี่ยวกับผู้มีสิทธิ์กู้เงินและแนวปฏิบัติในการตรวจสอบการใช้เงินทุนที่ชัดเจนจะทำให้มั่นใจได้ว่า การใช้เงินทุนเพื่อการลดการปล่อยมลพิษเป็นไปอย่างมีประสิทธิภาพ นอกจากนี้ สถาบันการเงินยังสามารถร่วมมือกับหน่วยงานภาครัฐและผู้บังคับใช้กฎหมายเพื่อสร้างแรงจูงใจเชิงนโยบายแก่ผู้ผลิตที่อยู่ระหว่างการเปลี่ยนผ่านไปสู่ CBAM ได้ ทั้งนี้ ปัจจุบัน ธนาคารหลายแห่งทั่วโลกได้เริ่มให้กู้ยืมเงินเพื่อสิ่งแวดล้อมแล้ว เช่น Deutsche Bank, OCBC, ธนาคารกรุงเทพ และ ธนาคารกสิกรไทย

2. ผู้ลงทุนในสตาร์ทอัพด้านเทคโนโลยีเพื่อสภาพภูมิอากาศ: สถาบันการเงินสามารถช่วยเร่งรัดการพัฒนาเทคโนโลยีได้โดยการร่วมลงทุนใน Startups ด้านเทคโนโลยีเพื่อสภาพภูมิอากาศผ่านบริษัทเงินร่วมลงทุนในเครือ (Corporate Venture Capital) ซึ่งไม่เพียงแต่เป็นการอัดฉีดเม็ดเงินลงทุนเข้าไปเท่านั้น แต่ยังช่วยส่งเสริมนวัตกรรมและการเติบโตในเทคโนโลยีเพื่อสภาพภูมิอากาศในภาพรวมด้วย ตัวอย่างของสถาบันการเงินที่ให้พันธสัญญาที่จะลงทุนเพื่อการลดผลกระทบ ได้แก่ HSBC และ ธนาคารกสิกรไทย

3. ผู้จัดการกองทุนเพื่อความยั่งยืน: สถาบันการเงินมีความสามารถในการบริหารจัดการกองทุนเพื่อความยั่งยืนสำหรับการลงทุนสาธารณะ โดยสามารถมุ่งเน้นการลงทุนในบริษัทที่มีส่วนเกี่ยวข้องกับการพัฒนาเทคโนโลยีเพื่อสภาพภูมิอากาศหรือบริษัทที่มีความมุ่งมั่นในดำเนินงานอย่างยั่งยืน การลงทุนเหล่านี้จะส่งเสริมให้เทคโนโลยีที่เป็นมิตรต่อสภาพภูมิอากาศและแนวปฏิบัติการดำเนินงานอย่างยั่งยืนเติบโต สถาบันการเงินที่ได้ดำเนินการในลักษณะนี้แล้ว เช่น ธนาคารยูโอบี, Blackrock, ธนาคารไทยพาณิชย์ และ ธนาคารกสิกรไทย

4. พันธมิตรในการให้บริการเทคโนโลยีเพื่อสภาพภูมิอากาศ: สถาบันการเงินสามารถร่วมมือกับผู้ให้บริการเทคโนโลยีเพื่อสภาพภูมิอากาศในการนำเสนอบริการ เช่น Carbon Accounting System ในราคาที่เหมาะสม เพื่อการลดปริมาณการปล่อยมลพิษได้ ความร่วมมือในลักษณะนี้จะช่วยให้ผู้ผลิตที่มองหาวิธีการลดการปล่อยมลพิษสามารถเข้าถึงเครื่องมือที่จำเป็นได้มากขึ้น

การสร้างเสริมทักษะความรู้ให้ผู้ประกอบการ

1. ผู้สอนและให้ความรู้: สถาบันการเงินสามารถจัดงานสัมมนาเพื่อให้ความรู้เกี่ยวกับ CBAM และหัวข้ออื่น ๆ ที่เกี่ยวข้องกับความยั่งยืนได้ ซึ่งโครงการเหล่านี้จะช่วยให้ผู้ผลิตมีความรู้ที่จำเป็นสำหรับการดำเนินการชดเชยการปล่อยคาร์บอนที่มีความซับซ้อน ตัวอย่างของสถาบันการเงินที่ได้ดำเนินการในลักษณะนี้แล้ว เช่น Commonwealth Bank of Australia, Santander Bank และ ธนาคารกสิกรไทย

2. ผู้ให้คำปรึกษาด้านความยั่งยืนแก่ลูกค้า: สถาบันการเงินสามารถอบรมให้ความรู้แก่ผู้ดูแลความสัมพันธ์ลูกค้าขององค์กรให้มีความเชี่ยวชาญด้านเทคโนโลยีเพื่อสภาพภูมิอากาศและ CBAM เพื่อให้คำปรึกษากับลูกค้า และให้คำแนะนำลูกค้าเกี่ยวกับแหล่งข้อมูลที่มีประโยชน์ รวมถึงผู้ให้บริการเทคโนโลยีเพื่อสภาพภูมิอากาศที่น่าเชื่อถือ ทั้งนี้ สถาบันการเงินที่ได้เริ่มดำเนินการแล้ว เช่น HSBC และ ธนาคารกสิกรไทย

3. ศูนย์รวมทรัพยากรทางเทคโนโลยีเพื่อสภาพภูมิอากาศที่น่าเชื่อถือ: สถาบันการเงินสามารถใช้ประโยชน์จากความรู้ความเชี่ยวชาญของบุคลากร และจากเครือข่ายขององค์กรเพื่อจัดทำรายชื่อผู้ให้บริการเทคโนโลยีเพื่อสภาพภูมิอากาศที่น่าเชื่อถือ ทั้งนี้ การที่สถาบันการเงินช่วยวิเคราะห์ธุรกิจของผู้ให้บริการเหล่านี้ในเชิงลึก และแนะนำรายชื่อให้กับผู้ผลิตที่ต้องการใช้เทคโนโลยีเพื่อลดการปล่อยมลพิษช่วยให้ผู้ผลิตมั่นใจได้ว่าจะได้รับบริการที่น่าเชื่อถือและมีประสิทธิภาพ

 

บทสรุป

ในช่วงของการก้าวเข้าสู่ยุค CBAM ผู้ผลิตไม่ได้อยู่ท่ามกลางการเปลี่ยนผ่านนี้เพียงลำพัง ยังมีผู้มีส่วนได้เสียจำนวนมากที่พยายามร่วมผนึกกำลังเพื่อก้าวผ่านความเปลี่ยนแปลงนี้ ไม่ว่าจะเป็น เจ้าของธุรกิจ วิสาหกิจขนาดกลางและขนาดย่อม สตาร์ทอัพ นักลงทุน หรือสถาบันการเงิน ความท้าทายและโอกาสที่เกิดจาก CBAM ไม่ได้เกิดขึ้นเฉพาะในอุตสาหกรรมหรือภูมิภาคใดภูมิภาคหนึ่ง แต่ส่งผลกระทบต่อทุกภาคส่วนทั่วโลก โดยเฉพาะอย่างยิ่งในวงการสตาร์ทอัพที่เป็นผู้นำในการใช้นวัตกรรมเพื่อช่วยให้ผู้ผลิตอยู่รอดในช่วงการเปลี่ยนผ่านนี้ สตาร์ทอัพที่ทุ่มเทเพื่อริเริ่มทำสิ่งเหล่านี้และมีวิสัยทัศน์ที่มองการณ์ไกลจะมีศักยภาพโดดเด่นในการแก้ไขความท้าทายที่ซับซ้อนของ CBAM บริการและความเชี่ยวชาญของสตาร์ทอัพ เหล่านี้จะเอื้อให้ผู้ผลิตไม่ต้องเสียเวลาสร้างเครื่องมือขึ้นมาใหม่ ซึ่งจะช่วยเร่งขับเคลื่อนให้ธุรกิจประสบความสำเร็จในการปฏิบัติตามหลักเกณฑ์และก้าวเข้าสู่การดำเนินงานอย่างยั่งยืนได้

แม้ว่าผลกระทบของ CBAM ในระยะแรกจะดูไม่รุนแรงมากนักสำหรับภูมิภาคที่ไม่ได้จำเป็นต้องพึ่งพาการส่งออกสินค้าที่ก่อให้เกิดคาร์บอนสูงไปยังสหภาพยุโรป ผู้มีส่วนได้เสียต้องตระหนักถึงภาพรวมการเปลี่ยนแปลงที่กำลังเกิดขึ้นทั่วโลกด้วย ยุคของภาษีคาร์บอนก่อนข้ามพรมแดนได้เริ่มต้นขึ้นแล้ว ประเทศต่าง ๆ เช่น สหรัฐอเมริกา กำลังพิจารณานำมาตรการลักษณะเดียวกันมาปรับใช้ โดยปัจจุบันประเทศสหรัฐอเมริกาอยู่ระหว่างการพิจารณาออกกฎหมาย Clean Competition Act ซึ่งจะส่งผลให้ผู้ผลิตต้องจ่ายภาษีคาร์บอนสำหรับการนำเข้าผลิตภัณฑ์ที่ก่อให้เกิดคาร์บอนสูง ทั้งนี้ รัฐบาลสหรัฐฯ คาดการณ์ว่าจะเริ่มบังคับใช้กฎหมายฉบับนี้ในปี 2567 ตัวอย่างผลกระทบที่อาจเกิดขึ้นต่อประเทศไทยหากมีการบังคับใช้กฎหมายฉบับนี้สามารถพิจารณาได้จากมูลค่าการส่งออกผลิตภัณฑ์ไปยังประเทศสหรัฐอเมริกา เช่น ในปี 2565 ประเทศไทยมีการส่งออกผลิตภัณฑ์เหล็กไปยังประเทศสหรัฐอเมริการวม 4,510 ล้านเหรียญสหรัฐ และการส่งออกผลิตภัณฑ์อลูมิเนียมรวม 1,433 ล้านเหรียญสหรัฐ การเปลี่ยนแปลงเข้าสู่ยุคแห่งการกำหนดราคาคาร์บอนที่กำลังเกิดขึ้นทั่วโลกแสดงให้เห็นถึงแนวโน้มค่าใช้จ่ายที่เพิ่มขึ้นจากการปล่อยมลพิษสำหรับภาคธุรกิจอย่างไม่สามารถหลีกเลี่ยงได้

ในปัจจุบัน ผู้ผลิตเริ่มหันมาปรับใช้เทคโนโลยีการผลิตที่ดีต่อสิ่งแวดล้อมมากขึ้นเพื่อตอบสนองต่อสถานการณ์ การเปลี่ยนแปลงนี้นับเป็นก้าวย่างที่สำคัญเพื่อมุ่งสู่อนาคตที่ยั่งยืน ถึงแม้ว่าจะก่อให้เกิดค่าใช้จ่ายที่สูงขึ้นต่อภาคธุรกิจในระยะเริ่มต้น ทั้งนี้ ค่าใช้จ่ายที่เพิ่มขึ้นจะทำให้ผลิตภัณฑ์มีราคาสูงขึ้นและส่งผลกระทบไปถึงผู้บริโภคในท้ายที่สุด เมื่อสังคมเริ่มมีความตื่นตัวในด้านสิ่งแวดล้อมเพิ่มมากขึ้น สาธารณชนมีแนวโน้มที่จะหันมานิยมผลิตภัณฑ์ที่ก่อให้เกิดคาร์บอนต่ำมากขึ้น ท้ายที่สุดแล้ว ความเคลื่อนไหวดังกล่าวจะช่วยปูทางสู่ตลาดที่คำนึงถึงความเป็นมิตรต่อสิ่งแวดล้อมมากขึ้น

การปรับตัวต่อมาตรการ CBAM และความท้าทายที่เกิดขึ้นช่วยให้หลายภาคส่วน ไม่ว่าจะเป็นผู้ผลิต สตาร์ทอัพ นักลงทุน หรือสถาบันการเงิน มีโอกาสทำงานร่วมกันเพื่อสร้างการเปลี่ยนแปลงในเชิงบวกสู่อนาคตที่สะอาดและยั่งยืนมากขึ้น ทั้งนี้ การปรับตัวเพื่อเปลี่ยนผ่านไปสู่เศรษฐกิจคาร์บอนต่ำไม่เพียงแต่จะสร้างความท้าทาย แต่ยังส่งเสริมให้ทุกภาคส่วนมีวิสัยทัศน์ในการดำเนินงานอย่างมีความรับผิดชอบและเป็นมิตรต่อสิ่งแวดล้อมมากยิ่งขึ้นด้วย

 

ผู้เขียน: เบญจมาศ ทู้สกุล

บรรณาธิการ: วรพจน์ กิ่งแก้วก้านทอง

 

แหล่งข้อมูลอ้างอิง:

  • https://www.consilium.europa.eu/en/press/press-releases/2022/03/15/carbon-border-adjustment-mechanism-cbam-council-agrees-its-negotiating-mandate/
  • https://kpmg.com/xx/en/home/insights/2022/08/carbon-border-adjustment-mechanism-impacts.html
  • https://taxation-customs.ec.europa.eu/carbon-border-adjustment-mechanism_en
  • https://www.pwc.ch/en/insights/tax/eu-deal-reached-on-the-cbam.html
  • https://www.europarl.europa.eu/legislative-train/package-fit-for-55/file-carbon-border-adjustment-mechanism
  • http://env_data.onep.go.th/reports/subject/view/128
  • https://watchwire.ai/5-carbon-accounting-challenges-and-how-address-them/
  • https://www.pwc.com/m1/en/services/tax/me-tax-legal-news/2023/eu-carbon-border-adjustment-mechanism.html
  • http://www2.ops3.moc.go.th/
  • https://mgronline.com/business/detail/9660000048165
  • https://carboncredits.com/congress-introduces-us-cbam-clean-competition-act/

Balancing the Promise of AI Innovation with the Hidden ESG Costs

Posted on by [email protected]

AI can become an incredibly valuable tool for humanity, but it’s vital for stakeholders to work together now to ensure that the technology is safe, sustainable, and equitable.  In this article, we will discuss some of the key concerns surrounding AI, and how startups, investors, and policymakers can address those concerns.

 

The Promise and Potential of AI

The past year has seen a boom in funding for AI startups.  Even as funding and valuations recede in the startup ecosystem as a whole, AI is a sector which has seen continual funding as investors are excited by the rapid advancements in the quality of the technology. 

Figure 1: Steady investment into AI Industry despite VC slowdown

Source: CB Insights

It’s clear that AI has matured to a level where it can replicate or enhance many tasks which are currently being performed by humans.  Already, institutions are leveraging AI to optimize everything from portfolio management to process automation, which can help boost performance and improve production costs.  Since the launch of GPT-4 earlier this year, Generative AI has captured the public imagination and given us new tools for creativity and productivity in our personal and professional lives. 

We can also see that tackling the world’s biggest issues such as financial inclusion and climate change will require the support of AI technology to not only accelerate the development of new solutions, but also to help deploy solutions at mass scale.  Many experts have identified lack of data as a huge barrier for the transition to a sustainable economy – without reliable data and the ability to understand it, businesses may not know how or where to adjust their operations, and investors will struggle to deploy capital efficiently into the most impactful solutions.  AI could be key solving these problems.  Starting all the way at the first step of decarbonization a business, identifying and measuring the business’s GHG emissions, AI could be used to automate calculations by scraping and analyzing the organization’s existing information.  This could emissions reporting feasible for SMEs who may lack the extra manpower or resources needed to manually evaluate their processes.  Algorithms trained on satellite and climate data could help monitor and identify problem spots, and give governments better tools to prepare for natural disasters before they occur.  Deloitte has identified other areas where AI could be used to drive sustainability, including “energy modelling for infrastructure optimization and urban planning; utilizing diverse data sources for environmental monitoring and targeted sustainability…; and the acceleration of climate science (physics emulators, climate forecasting, materials science).” [1]

From a social perspective, properly trained AI could revolutionize accessibility to healthcare and education.  As will be discussed later in greater detail, use of AI in this space has several potential pitfalls, but there is still great opportunity for AI to serve the public good.  One could imagine a world where chatbots and talkbots can take over many routine questions and screening in healthcare, freeing up valuable human resources to focus their efforts on patient care.  AI algorithms have already been developed to assist doctors in analyzing medical data and diagnosing illnesses; speed and efficiency are important factors in reducing the cost of healthcare.  Similarly, many startups are looking to use AI and technology to improve access to quality education, whether by providing tools that help teachers plan their curriculum and manage classrooms, or by developing applications to help students learn in non-classroom settings.

Despite the immense promise of AI technology, there remain many hidden or unrecognized costs related to the use of AI, from both a social and environmental perspective.  Startups and investors alike need to be aware of the ESG risks associated with AI, and how those risks affect their future value generation.

 

Hidden Environmental Costs

From a sustainability perspective, the development of generative AI models in particular presents a problem.  Generative AI models are trained on massive amounts of unstructured data, which requires massive amounts of computing power.  As a result, training new models consumes high amounts of energy (to power the computers and servers) and water (used in cooling systems to ensure the computers continue to run at high capacity). 

Researchers have estimated that training a single large-language model (LLM) could emit more than 626,000 pounds of carbon dioxide emissions (CO2e).[2]  For reference, the average human is only responsible for roughly 11,000 tons of CO2e across a full year.  According to the researchers’ analysis, the process of fine-tuning these types of large models is responsible for much of the estimated emissions, which presents an ethical quandary as fine-tuning is necessary for ensuring the accuracy and usability of an AI model, not to mention giving data scientists the opportunity to test and adjust the models to ensure the results are safe and equitable.

Figure 2: Carbon Emissions of Model Training, Relative to Benchmarks

Source: MIT Technology Review, Strubell et. al.

While carbon emissions may be one of the more pressing concerns for investors and financial institutions (particularly with increasing regulatory pressure regarding emissions reporting and taxation), training AI models also has an effect on water consumptions and scarcity.  Researchers have estimated that training AI models such as GPT-3 consumed roughly 700,000 liters of freshwater.  Large tech giants such as Microsoft and Google have already begun paying increased attention to water consumption as a key environmental risk in their sustainability reports.

 

Social and Ethical Considerations

There have been numerous concerns raised regarding the further development and usage of AI technology.  Some concerns are specific to AI, including problems related to data privacy, model biases, and algorithmic decision making.  Other concerns are more philosophical in nature, such as the impact of AI on jobs and society.  Such issues have been endemic to technological innovation throughout history, but given the immense changes in work and life that may result from AI, it remains vital for stakeholders to consider how best to face these challenges.

In the past few years as AI algorithms have been deployed at increasing large scale, multiple companies have run into problems when the technology has produced unforeseen results.  For example, in 2016 Microsoft launched an experimental chatbot Tay; within 24 hours of launch, Tay had learned to produce profanity, racist comments, and controversial political statements.[3]  More recently, a US lawyer was fined for using ChatGPT to prepare citations for a court filing when it was discovered that ChatGPT had fabricated some of the cases cited.[4]  In a world where disinformation is already rampant throughout the internet, the emergence of generative AI models trained on massive amounts of data scraped from the internet raises concerns not only regarding accidental production of problematic content, but also intentionally malicious use of AI technology.  Earlier this year, Check Point Research found that cybercriminals have begun experimenting with generative AI to more quickly develop malware or other cyber-attacks, despite their lack of programming skills.[5]

Use of AI in products such as digital lending, biometrics, and health diagnostics can also cause real harm to society if developers are not careful in screening for biases.  Complex models like neural networks and deep learning are inherently a black box – it is difficult for people to understand how they make decisions.  Black boxes are particularly susceptible to societal biases embedded in datasets used to train the model – research by ProPublica in 2016 revealed that machine learning-based risk assessments used in several jurisdictions for criminal sentencing were not only unreliable at predicting future crimes, but that the model’s errors were also racially biased.  ProPublica’s analysis of the model’s performance found that black defendants were more likely to be misclassified as high risk, whereas white defendants were more likely to be misclassified as low risk.[6]   In the field of health diagnostics, many datasets available to be used in training AI models contain primarily data on men, which creates inaccuracies when those same models are applied to predict illness or medical conditions for women.[7]

Apart from the problems with imperfect algorithms, many people are also concerned about the problems presented by the use of AI to replace human labor.  This problem is not new, and has occurred over and over again throughout history with new technological developments.  Economists consider “technological unemployment” as one of key types of structural unemployment.  In this past, technological unemployment has been a temporary issue, as economies adapt and the labor force is able to reskill and retrain for new roles generated by advancements in technology.  The fear however, is that advancements in AI and automation in this era may create a more permanent level of unemployment as companies are able to outsource an increasing number of tasks to computer algorithms.  This concern is likely to disproportionately affect workers in emerging markets whose jobs may be the easiest to replicate using AI.[8]

Figure 3: Rapid acceleration of technological development

Source: https://blogs.microsoft.com/on-the-issues/2023/05/25/how-do-we-best-govern-ai/

 

What Can We Do About This?

There is no easy solution to any of these issues, but the potential of AI to create real positive impact on human life makes it vital for stakeholders to deeply consider how to balance the ethical and societal concerns as well as the negative environmental impact of AI development.

Many industry experts already espouse the principles of Responsible AI; tech giants like Microsoft and Google who are deeply involved in AI development and research provide guidance and tools for developers to embed these principles into their workflow.  The Responsible AI Institute is a non-profit which supports organizations with tools to develop and identify safe and trustworthy AI products.  Different organizations may adhere to different principles to ensure fairness, though similar concepts are found throughout the AI development industry.

Figure 4: PwC’s Responsible AI Framework

Source: PwC, Responsible AI – Maturing from theory to practice

Such frameworks may not be perfect (and the increasing complexity of AI algorithms makes them inherently prone to unexplainable errors), but they do represent a baseline of how startups can build trust and confidence with users and investors alike.  Providing transparent documentation regarding data acquisition and sourcing, as well as model development and testing is key to ensuring that people do not feel like their privacy has been violated, and that they can fully trust the results produced by the system.  Surveys by PwC also indicate that while many organizations are rapidly adopting such ethical principles in relation to their usage of AI technology, only 27% “are actively incorporating ethical principles in [their] day-to-day operations.”[9]  Accordingly, many startups have seen this as a business opportunity, to create the tools necessary for other developers to improve their AI governance standards.

Figure 5: Responsible AI Startups

Source: CB Insights

Similarly, there are several recommendations for companies looking to decrease the emissions and resource consumption from their AI projects.  These include:

  1. Leveraging and tuning existing models instead of training new models from scratch. Given the immense environmental cost associated with training new generative models, one of the simplest ways for companies to lower their environmental impact is simply to not train a new model, instead adapting pre-trained models for the company’s specific use case.
  2. Implementing efficient and energy-conserving computational methods. As energy usage is one of the key drivers of environmental impact, adopting low power computational approaches like tinyML[10] or efficient learning techniques like PARSEC[11] can help companies reduce both costs and energy consumption.
  3. Optimizing resource usage through selection of cloud providers and data centers. By shifting energy-intensive training to servers powered by clean energy, or which are located in places with more resource-efficient cooling systems, companies can lower their overall emissions and water usage.  Depending on location, data centers may use significantly less freshwater, or may be linked to energy sources such as hydroelectric instead of fossil fuels.

The question of AI’s impact on human society may be both the most concerning issue and the issue which is hardest to find a solution to.  Investors, policymakers, and startups alike need to consider the question of how we want AI to shape the future of human history, and how their actions will support the creation of AI products that are beneficial to humanity (as opposed to supporting the rise of Skynet).  Many startups like Wiz.AI or Grammarly have developed AI tools that help human workers boost their productivity, as opposed to replacing human workers entirely.  Ensuring that AI development is focused around augmenting human capabilities is one of the key ideas for mitigating some of the potential social downsides of technological development.  This is also significant for ensuring the continued safety of AI development; one of Microsoft’s core principles for ethical AI development is to “always ensure that AI remains under human control.”[12]

For investors and financial institutions, it is important to embed these ideas into the due diligence process when considering whether to fund a new AI startup or project.  Considering the value proposition of a project is already a key part of an investment decision, but in the context of AI investments, investors should be screening for whether the target is aware of the ESG risks and taking steps to mitigate those risks.  Given the environmental costs (which may remain significant regardless of any mitigation plans), investors should also be considering whether the value proposition is sufficient to justify the costs.  Regulators are already moving in favor of greater accountability surrounding ESG reporting and the reduction of financed emissions, thus it is the responsibility of investors to deeply consider how to balance these risks.

Finally, it is important to note that policymakers will need to play a large role in setting reliable standards and guardrails around the AI industry.  It is clear that adopting many of the principles discussed above may require high effort and investment, particularly in the short run, and companies may be reluctant to do so without pressure from investors or regulators.  By working together with industry experts, policymakers should be able to identify the best practices for safeguarding the future of the world, and to enforce those standards across the entire industry.

 

Conclusion

While some may consider the issues raised in this article as evidence of the danger that AI presents to human life and reason enough to put a halt to AI development, we continue to believe that we are not doomed to a dystopian future.  There is immense potential for AI to benefit the future of humanity, to support us in figuring out new and innovative solutions for a more equitable and more sustainable world.  Nevertheless, there are real costs associated with AI in the present day, and it is incumbent on startups, investors, and policymakers alike to ensure that we are aware of these costs and incorporate them into our decision making.  Our actions today, and the degree to which we are willing to strive for responsible action, will dictate how the development of AI and human life plays out in the future.  The recommendations discussed here for how different stakeholders should consider balancing ESG risk when developing new AI products are not exhaustive or set in stone, and we firmly believe that continued research, investment, and conversation surrounding these issues is necessary for ensuring the safety, sustainability, and equitability of AI technology.

 

Author: Krongkamol deLeon

Editor: Woraphot Kingkawkantong

 

 

Endnotes:

[1] https://www2.deloitte.com/uk/en/blog/experience-analytics/2020/green-ai-how-can-ai-solve-sustainability-challenges.html

[2] https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/

[3] https://www.bbc.com/news/technology-35890188

[4] https://www.unsw.edu.au/news/2023/06/this-us-lawyer-used-chatgpt-to-research-a-legal-brief-with-embar

[5] https://research.checkpoint.com/2023/opwnai-cybercriminals-starting-to-use-chatgpt/

[6] https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

[7] https://www.forbes.com/sites/carmenniethammer/2020/03/02/ai-bias-could-put-womens-lives-at-riska-challenge-for-regulators/?sh=b060094534f2

[8] https://futurism.com/un-report-robots-will-replace-two-thirds-of-all-workers-in-the-developing-world

[9] PwC, Responsible AI – Maturing from theory to practice

[10] https://www.tinyml.org/

[11] Probabilistic Neural Architecture Search, https://hkaift.com/a-deep-dive-into-efficient-ai/

[12] https://blogs.microsoft.com/on-the-issues/2023/05/25/how-do-we-best-govern-ai/

 

Sources:

https://hbr.org/2023/07/how-to-make-generative-ai-greener#:~:text=AI%20(and%20software%20in%20general,75%25%20reduction%20in%20operational%20emissions.

https://blogs.microsoft.com/on-the-issues/2023/05/25/how-do-we-best-govern-ai/

https://earth.org/the-green-dilemma-can-ai-fulfil-its-potential-without-harming-the-environment/#:~:text=Behind%20the%20scenes%20of%20AI’s,gas%20emissions%2C%20aggravating%20climate%20change.

https://www.scientific-computing.com/analysis-opinion/true-cost-ai-innovation

https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/

https://www.ey.com/en_in/ai/how-generative-ai-can-build-an-organization-s-esg-roadmap

Strubell et. al., “Energy and Policy Considerations for Deep Learning in NLP.”

Li et. al., “Making AI Less Thirsty: Uncovering and Addressing the Secret Water Footprint of AI Models.”

OECD Digital Economy Papers, “Measuring the Environmental Impacts of Artificial Intelligence Compute and Applications: The AI Footprint.”

Decoding CBAM: Navigating the Realm of Cross-Border Carbon Adjustment Mechanism

Posted on by beaconvcadmin

In pursuit of its visionary aim to lead the charge towards climate neutrality by 2050, the European Union (EU) has embarked on a transformative journey. One of its recognizable pioneering initiatives to curb the Greenhouse Gas (GHG) emissions has materialized since 2005 when it implemented the carbon pricing mechanism on EU corporations through the EU ETS cap-and-trade system. This system, however, has brought about disadvantages to manufacturers in the EU, leading to unfair competitive edge for companies in regions with more relaxed environmental regulations.In 2019, the European Green Deal was established to further enhance and accelerate the EU’s climate and sustainability efforts. The European Green Deal encompasses a wide range of strategies and measures, among which stands the Cross-Border Carbon Adjustment Mechanism (CBAM), which in one way will assist the EU in realizing the climate neutrality goal, and in another way will protect EU industries from unfair competitions caused by higher environmental costs.

CBAM, an acronym that carries immense significance for businesses, policymakers, and stakeholders around the globe, represents a pivotal component of the EU’s commitment to addressing climate change. As we delve into the intricacies of CBAM, we’ll unravel its impact on various industries, explore its timeline and implications, dissect the methods employed for emission calculation under CBAM standards, and delve into the ways manufacturers can measure, reduce, and offset emissions in the face of this transformative mechanism.

But CBAM is not just a challenge; it’s also an opportunity. As manufacturers navigate this dynamic landscape, financial institutions find themselves in a position to offer critical support and expertise. Together, they can accelerate the transition towards a cleaner, more sustainable future. Join us on this journey as we decode CBAM and illuminate the path forward in the realm of cross-border carbon adjustment.

What CBAM is

CBAM is a part of several initiatives introduced under the European Green Deal in support of the goal to be the first climate-neutral continent by 2050. Following that aspiration, the EU is imposing various carbon tax regimes on European manufacturers, and introducing CBAM to even the playing field and eliminate price advantage for imported products from regions where carbon measurements may not be as stringent. In short, CBAM is a carbon tax applied on carbon-intensive products imported into the European Union, the amount equivalent to the tax applied to identical domestic goods for the same amount of GHG being emitted.

During its initial phase, importers will only need to report the emissions associated with the imported goods. However, in the later stage of the regulation, importers will have to purchase CBAM certificates to compensate for any difference in the carbon price paid in the country of origin as compared to the carbon price charged to producers in the EU. To implement this framework, the following data will need to be collected by importers:

    1. Total quantity of imported products
    2. Carbon price paid for the product in the country of origin
    3. Actual direct and indirect emissions of GreenHouse Gas (“GHG”) of the imported products

CBAM’s scope and timeline

The CBAM’s transitional phase will be enforced from October 2023 to December 2025. During this phase, importers will only need to report the data related to their imports as specified above. No data verification or purchase of CBAM certificates is needed. The scope of products covered during this initial phase is 6 emission-intensive sectors which are more susceptible to a risk of carbon leakage: cement, aluminum, fertilizers, iron and steel, electricity, and hydrogen.

The permanent system will enter into force in January 2026. Importers will not only be required to report CBAM-related data, but will also be required to have the data verified by an accredited verifier and purchase CBAM certificates for any gap in the carbon price paid. An extension of the product scope for CBAM after the transitional phase will be reviewed to assess practicality and feasibility of such inclusion. Potential product categories to be covered in the second phase include organic chemicals, plastics, and ammonia. The extension is planned for full implementation by 2030.

 

Deconstructing calculation of GreenHouse Gas emission under CBAM

The scope of GHG emission under CBAM guideline closely aligns with the emission scope set out by the GHG Protocol Corporate Standard (“GHG Protocol”). The GHG Protocol distinguishes between three scopes of emissions:

Source: Zevero

  • Scope 1 refers to GHG emission from own operation or asset that the company emits directly such as use of fossil fuel energy used in production or transportation
  • Scope 2 refers to GHG emission that company indirectly emits from supporting business activities such as electricity used in air conditioning or lighting
  • Scope 3 refers to all GHG emission that the company may induce along its value chain, such as providing financing to GHG emitting businesses or the purchase of office supplies that may emit GHG during production

Source: European Commission

More specifically, direct emissions under CBAM equals to scope 1 emissions under the Greenhouse Gas Protocol, including GHG emitted directly during the production from combustion of fuel, or other byproduct emission incurred from material chemical reaction or heating and cooling process critical to the production. Accounting for the emission can either be based on actual measurement of the emission or calculation of emission using emission factor.

Indirect emissions under CBAM covers scope 2 and scope 3 of the GHG Protocol. Scope 2 reporting under CBAM only concerns electricity consumed during the manufacturing process of products, such as the lighting and air conditioning of the plant. As for scope 3, the biggest emission which is toughest to measure, CBAM only requires importers to report emissions from manufacturing of precursor input materials which are already under CBAM scope (cement, iron/steel, aluminum, hydrogen, and fertilizers). This initial stage does not necessitate complicated accounting of emission from activities like employee commuting or customer use of products. For more details on how to measure emissions for each sector, please find the European Commission’s guidance here.

 

CBAM: Shaping the Future of Trade and Sustainability – Implications for the Thai Economy

According to the Office of Industrial Economics, in 2022 Thailand exported $201 million of iron and $111 million of aluminum to countries in the EU. Although these only represented 1.3% of the total export in 2022, the scope of the extended CBAM covering other categories such as plastics will have greater impact as it accounts for $676 million or 2.4% of the total export.

All exporters for the above product categories will have a responsibility to report emissions to the EU, except products with value below €150 or products used in the military. After the transition period, Thai exporters will face an additional process of submitting the report to accredited verifier before the report will be deemed valid. They will also have to pay an additional cost of carbon tariff to the CBAM, net of any amount already paid in countries of origin.

Economic disadvantages to the Thai exporters

The price of carbon in the EU Emission Trading System, according to Statista, that will be used for carbon price reference in the initial implementation stage, has been fluctuating in the range of €80-€100 per ton of CO2 during the first half of 2023. The price is predicted to jump even higher when the full CBAM mechanism comes into effect as there will be more demand to purchase allowances under the EU ETS when the free allowances gradually phase out. One way to illustrate how this translates into economic disadvantage for Thai exporters is to calculate the difference in emission cost per ton of product between Thailand and other competing exporters. Examples of carbon tariff comparison for iron products and primary aluminum are shown in the table below.

  Iron: Thailand Iron: Global Iron: EU Aluminum primary: Thailand Aluminum primary: Global Aluminum primary: EU
1Carbon price (USD/tCO2e) 96.3 96.3 96.3 96.3 96.3 96.3
2Emission (tCO2e/ton) 1.55 1.40 1.14 12.24 12.50 6.20
Carbon Tariff (1*2) (USD) 149.48 134.82 109.78 1,178.32 1,203.75 597.06
Compare to Thailand (%) N/A -10% -27% N/A 2% -49%

As can be seen from the table above, Thai exporters are at a disadvantage in terms of higher carbon tariff. This is even more accentuated when compared to the EU manufacturers. This additional cost which will eventually lead to higher price charged to the European buyers, or the exporter will have to take profitability hit by absorbing the increased cost. This will likely shift Thailand’s export of CBAM goods to other locations outside of the EU in the short- to -medium term, given that there’s other buyers. However, manufacturers will need to gradually upgrade their production technology to a greener one to stay afloat as other countries like the United States will soon be implementing a similar mechanism to curb GHG emissions.

Current progress from Thai government agencies

In response to this significant change, the Department of Trade Negotiations (DTN), the Federation of Thai Industries (FTI), and the Thai Greenhouse Gas Management Organization (TGO) collaborated to host a seminar on CBAM. This seminar aimed to educate stakeholders and gather feedback regarding concerns during the initial implementation phase. Thai manufacturers have specifically requested support for reporting technology, permission to utilize Thai accredited verifiers for cost-effective report verification, and leniency in penalties for unintentional reporting errors during the adaptation phase. Currently, the DTN and FTI are engaged in discussions with EU representatives to explore potential solutions that can mitigate adverse impacts on Thai industries. We anticipate learning more about the outcomes in the near future.

Earlier this year, TGO made a significant stride by forging a collaborative partnership with the Ministry of Higher Education, Science, Research, and Innovation, in conjunction with five prestigious universities, including Chulalongkorn University and Thammasat University. Together, they are crafting an innovative curriculum tailored for sustainability professionals, equipping them with specialized knowledge in carbon footprint management and carbon credit utilization. This educational initiative aims to provide invaluable support for businesses as they transition seamlessly into the CBAM.

In addition to this pioneering educational endeavor, TGO is also developing a platform designed for embedded emission calculation. This platform serves as a vital tool to assist Thai manufacturers in accurately reporting carbon emissions, thereby ensuring compliance with CBAM regulations. The platform is now in the pilot testing phase with the active participation of several volunteer companies. Once fully realized, this forward-thinking initiative is poised to significantly reduce costs associated with emission reporting for Thai exporters.

 

Navigating the Transition: Challenges and Manufacturers and Opportunities for Startups

To remain competitive in the long term, manufacturers must address three key activities: measuring GHG emissions accurately, reducing GHG emissions effectively, and transacting carbon offsets. Each of these activities comes with its own implementation challenges, which startups can seize as opportunities to provide solutions.

1. Accurate Measurement of GHG Emissions

Accurate measurement of emissions serves as the bedrock of CBAM and any effective emissions reduction strategy, echoing the timeless wisdom of Peter Drucker: “You can’t manage what you can’t measure.” Worldwide, startups are diligently working to address this pivotal task by introducing innovative solutions through carbon accounting platforms. Prominent players in the carbon accounting space, such as Terrascope, RIMM, Unravel Carbon, and others, have emerged to tackle this challenge head-on. In Thailand, TGO is actively engaged in the development of an embedded emission calculation platform, poised to bring substantial advantages to Thai exporters upon its completion. However, it’s crucial to acknowledge that this endeavor is anything but straightforward, as it grapples with a multitude of formidable challenges. To shed light on this complexity, let us explore some of the most salient hurdles.

1.1  Lack of Data Integrity which mainly stems from lack of accuracy in measuring scope 3 emissions and difficulty standardizing data from various sources such as different equipment types and different factories. Nonetheless, integrating these features into carbon accounting tools can both improve accuracy and reduce standardization problems.

1.1.1 Carbon calculators using emission factors (EF) – unlocking the power of granularity: One of the fundamental hurdles in accurate GHG emission measurement lies in the granularity and availability of data. EFs are industry-specific proxies that can be used to estimate the actual carbon emission, such as the amount of raw materials used or the production method that the manufacturer adopts.

1.1.2 Integration across supply chain – connecting the dots: To achieve accurate GHG emission measurement, we must track a product’s entire lifecycle, extending beyond individual factories to encompass a web of suppliers. Integrated supply chain tracking is the key. Imagine a system where emissions data from every supplier seamlessly merges into a comprehensive picture. This integration ensures the accurate measurement of scope 3 emissions, offering a holistic view of a product’s carbon footprint.

1.1.3 Blockchain carbon ledger – enhanced data integrity: Data integrity is paramount in GHG emission measurement. Inaccuracies often arise due to a lack of trust and transparency. Enter blockchain technology, promising enhanced verifiability and transparency. A blockchain carbon ledger securely records, time-stamps, and links emission-related transactions across a decentralized network. This not only bolsters the credibility of reported data but also allows stakeholders to trace emissions data origins precisely.

1.2  Labor Intensiveness. Current practice of emission measurement involves extensive manual processing which leads to excessive costs and is prone to errors. However, these can be solved by using automation and technologies.

1.2.1 Integration with systems and equipment – the power of automation: One of the pressing challenges in current emission measurement practices is their labor-intensive nature, often leading to high costs and error-prone outcomes. By integrating carbon accounting systems with tools like IoT (Internet of Things), ERP (Enterprise Resource Planning), or machinery, businesses can unlock the potential for automatic data retrieval based on activities. This shift towards automation significantly reduces the manual workload, making the process more efficient and cost-effective.

1.2.2 Optical Character Recognition – bridging the digital-physical gap: Physical documents have long posed challenges in the realm of GHG emission measurement due to their manual processing requirements. However, optical character recognition (OCR) technology provides a compelling remedy. OCR can automatically transform physical documents into digital formats, opening the door to efficient, automated processing for emissions calculations. By digitizing these documents, the entire process becomes faster, more accurate, and cost-effective.

2. Effective and Sustainable Emissions Reduction

Once emissions are accurately measured, manufacturers must reduce emissions during their processes to lower carbon tariff payment. However, this is hampered by technical limitations and economic constraints.

2.1  Technical Limitations. Addressing technical limitations in emissions reduction calls for a multifaceted strategy. Manufacturers grapple with three core challenges: the search for durable green material alternatives, the need for an ample supply of renewable energy for energy-intensive industries, and the imperative to mitigate high-emission manufacturing processes. In response, ongoing research and studies are relentlessly pursuing innovative solutions that encompass various aspects of emission reduction.

2.2.1 Innovative materials – pioneering more sustainable inputs: Promising alternatives are emerging, exemplified by initiatives like ELYSIS‘ development of inert anodes for aluminum smelting or HARBOR Aluminum‘s dedication to recycled materials. These innovative materials aim to reduce emissions and improve sustainability across industries.

2.2.2 GHG compound mitigation – preventing emissions at the source: Another avenue of exploration is the development of technologies that mitigate the formation of greenhouse gas compounds during manufacturing processes. Initiatives such as Analytics Shop‘s work on nitrification inhibitors in fertilizers and Hybrit‘s pursuit of hydrogen reduction in iron ore processing hold promise in this regard.

2.2.3 More efficient facilities management – smarter operations: Smart buildings, pioneered by companies like AltoTech, TIE-Smart, and Zenatix, are reshaping facilities management by optimizing energy usage and reducing emissions, contributing to sustainable manufacturing.

2.2.4  Use of renewable energy – filling the energy gap: Addressing the shortfall in renewable energy supply, especially for energy intensive industries, is critical. According to the Office of Natural Resources and Environmental Policy and Planning, Thailand, for instance, lags behind the EU, with only 11% of its energy consumption sourced from renewables in 2021. Thailand still has a lot of room to grow when compared to almost 40% in the EU. Providers like Clover Power and First Korat Wind offer renewable energy solutions that can help bridge this gap.

2.2.5 GHG capture technologies – seizing emission: Manufacturing plants are notorious sources of greenhouse gas emissions, particularly carbon dioxide (CO2), which is a primary contributor to global warming. By capturing these emissions at the source, we prevent them from being released into the atmosphere and exacerbating the greenhouse effect. Solutions such as carbon capture, utilization, and storage by Technip and Linde, along with direct air capture technologies developed by companies like Carbon Engineering and Climeworks, play a crucial role in capturing and mitigating emissions right at their source.

2.2  Economic Constraints: The economic constraints that often accompany the adoption of new and cleaner technologies pose significant challenges for manufacturing businesses in their efforts to reduce their carbon footprint. These constraints can manifest as high upfront investment costs, the need for workforce reskilling, and downtime of factories. However, manufacturers have several valuable options to navigate these economic challenges while advancing their sustainability objectives:

2.2.1 Sustainable Finance – accessing transitionary funding: One key strategy is to tap into sustainable finance options. Providers like GoParity and BluePath Finance offer gateways to sustainable financing, facilitating access to the capital necessary for investing in cleaner technologies. This approach not only aids in overcoming the initial financial hurdle but also aligns with broader sustainability goals.

2.2.2 Data Analysis and Tools for Emission Optimization – maximizing return on impact investment: In a world of budget constraints, where companies must make strategic decisions about where to invest for emissions reduction, data analysis and specialized tools play a pivotal role. The goal is to pinpoint precisely where within operations every dollar spent will yield the most significant reduction in carbon emissions. This approach empowers business owners to prioritize their budget effectively and make informed decisions about which areas to target for maximum impact on their carbon footprint.

2.2.3 Supply Chain Analysis – connecting the dots: Supply chain analysis plays a critical role in managing economic constraints while reducing carbon footprints. Manufacturers can seek out low-emission suppliers for raw materials within the scope of the Carbon Border Adjustment Mechanism (CBAM). Companies like Pantas and Terrascope specialize in supply chain analysis, helping manufacturers make informed decisions about sourcing materials from environmentally responsible suppliers.

3. Transaction of Emission Offsets

As manufacturers progress through the carbon reduction journey, the third crucial step involves addressing the remaining emissions either by making carbon tariff payments or through the purchase of carbon credits. While both options require a financial commitment, payments for carbon emissions to local organizations that support green initiatives within their own country is often the preferred choice. However, this stage introduces complexities that demand specialized knowledge to navigate effectively. Additionally, the EU has yet to announce clear regulations on framework for carbon credit purchase. We must closely monitor the forthcoming frameworks set to be released in the second quarter of 2025.

If permitted by regulation, the process of purchasing carbon credits operates within specific parameters. Manufacturers are likely to be permitted to purchase carbon credits, which can be deducted from their overall carbon tariff liability. The exact quantity and conditions of allowable carbon credits is subject to CBAM regulations and may vary based on factors such as industry type and historical emissions records.

Providers specializing in carbon credit exchanges, such as T-VER and Climate Impact X, along with renewable energy credit exchanges like Innopower, typically offer comprehensive guidelines and training to support manufacturers throughout this journey. The official CBAM website is also a valuable resource for staying informed about the latest terms and regulations updates. These resources help businesses navigate the complex landscape of carbon offset transactions, ensuring compliance with CBAM requirements and contributing to their sustainability goals.

Financial institutions’ roles to facilitate smooth transition to CBAM

As manufacturers embark on the multifaceted journey of carbon reduction and compliance with the Carbon Border Adjustment Mechanism (CBAM), they encounter a diverse range of challenges. From the meticulous measurement of greenhouse gas emissions to the implementation of innovative technologies for emission reduction, and finally, to navigating the complexities of carbon offset transactions, each step poses unique hurdles.

However, at the heart of these challenges lies a common thread: the need for financial resources to support the development and adoption of sustainable climate technologies. In the initial phases of measuring and reducing emissions, manufacturers often grapple with the financial burden of investing in new tools, processes, and infrastructure. This financial strain can be a significant barrier to progress.

On the other end of the spectrum, when it comes to carbon offset transactions, manufacturers face challenges rooted in knowledge gaps. The intricacies of purchasing carbon credits, understanding CBAM regulations, and effectively managing emissions offset strategies can be daunting without the necessary expertise.

This is where financial institutions (FIs) play a pivotal role in facilitating a smooth transition into CBAM. FIs are well-positioned to address both of these critical challenges.

Addressing Funding Challenges for Climate Technologies:

1. Provider of Low-Interest Green Loans: FIs can act as a lifeline by offering low-interest green loans to both retail customers and corporations looking to fund the development and implementation of climate-friendly technologies. Establishing clear eligibility criteria and monitoring guidelines for the use of these funds ensures they are directed toward emission reduction effectively. FIs can even collaborate with government agencies and regulators to design more favorable incentives at a policy level, specifically tailored to manufacturers under CBAM transition. Many banks worldwide are already participating in this green loan initiative, for example, Deutsche Bank, OCBC, BBL, and KBank.

2. Investor in Climate Tech Startups: FIs can further accelerate technology development by investing in climate tech startups through corporate venture capital arms. These investments not only inject capital but also foster innovation and growth within the climate tech sector. Examples of FIs who already committed funds for impact investing include HSBC and KBank.

3. Manager of Sustainability Funds: FIs have the capability to manage sustainability-focused funds designed for public investment. These funds can target companies that are actively involved in climate tech development or adhere to sustainability best practices. Such investments promote the growth of climate-friendly technologies and sustainable practices. FIs who are already in the space include UOB, Blackrock, SCB, and KBank.

4. Partner of Climate Tech Solution Providers: Collaborating with climate tech solution providers, FIs can offer emission reduction solutions, such as carbon accounting systems, at accessible prices to their clients. These partnerships expand the availability of essential tools for manufacturers seeking to reduce emissions.

Addressing Knowledge Gaps for Carbon Offset Transactions:

1. Trainer and Educator: FIs can organize knowledge-sharing sessions and seminars focused on CBAM and other sustainability-related topics. These educational initiatives empower manufacturers with the knowledge required to navigate the complexities of carbon offset transactions effectively. Examples of FIs who are already active in sharing knowledge with the public include Commonwealth Bank of Australia, Santander Bank, and KBank.

2. Sustainability Advisor to Clients: FIs can equip their relationship managers with expertise in climate tech and CBAM. This enables them to provide informed advice to clients and guide them toward valuable sources of information and third-party climate tech solution providers. FIs such as HSBC and KBank have already pledged resources to help advise clients in this field.

3. Resource Hub for Trustworthy Climate Tech Solutions: Leveraging their knowledge and extensive networks, FIs can curate a list of trustworthy third-party climate tech solution providers. Conducting due diligence on these providers and referring them to clients in need of emissions reduction solutions ensures that manufacturers receive reliable and effective assistance.

 

Closing thoughts

In the approaching era of CBAM, manufacturers are not embarking on this transformative journey alone. There is a collective effort involving various stakeholders, including business owners, SMEs, startups, investors, and financial institutions, all gearing up to navigate the changing landscape. The challenges and opportunities presented by CBAM are not isolated to a single industry or region; they resonate globally.

Startups, in particular, stand at the forefront of innovation to assist companies during this transition. Their pioneering spirit and fresh perspectives can play a crucial role in addressing the complexities of CBAM. By leveraging the solutions and expertise of these startups, manufacturers can avoid the need to reinvent the wheel, accelerating their path to compliance and sustainability.

While the initial impact of CBAM may seem modest for regions less reliant on carbon-intensive exports to the EU, it’s essential to recognize the broader shift occurring worldwide. The cross-border carbon tax era is dawning, with countries like the United States considering similar measures through legislation such as the Clean Competition Act, which will impose carbon taxes on carbon-intensive products imported into the country. This legislation is planned for enforcement in 2024. Iron products exported to the US in 2022 totaled $4,510 million and aluminum products totaled $1,433 million. This global movement towards carbon pricing underscores the inevitability of higher costs associated with emissions.

Manufacturers, in response, are already redirecting their efforts towards greener manufacturing technologies. While this shift may initially impact costs, it serves as a crucial step towards a more sustainable future. Ultimately, these higher costs will be reflected in the products reaching end consumers. As awareness grows and preferences evolve, the public is increasingly inclined to favor low-carbon products, paving the way for a more environmentally conscious marketplace.

In embracing CBAM and its associated challenges, there is a collective opportunity for positive change. Manufacturers, startups, investors, and financial institutions are poised to collaborate in shaping a cleaner and more sustainable future. As we collectively adapt to this new era, the transition towards a low-carbon economy offers not just challenges but a compelling vision of a greener, more environmentally responsible world.

 

Author: Benjamas Tusakul

Editor: Woraphot Kingkawkanthong

 

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