Introducing the Bursa MY ESG Disclosure Readiness Assessment Tool brought by ESG Disclose & Leadernomics
Distinguished as the sole automated software solution of its kind in Malaysia, our tool empowers clients to evaluate their current ESG initiatives, data collection processes, and preparedness to meet Bursa Malaysia's requirements. With our solution, there is no need to engage costly ESG consulting firms or assemble a team of consultants to manually conduct a gap analysis of your existing ESG practices.
In just a few days, our seamless and user-friendly tool simplifies the process by prompting you to answer the Bursa ESG Disclosures. It also provides illustrative examples of the data you should input to fulfil the disclosure criteria. Subsequently, our team of ESG SMEs examines this information to generate a comprehensive gap analysis report, identifying key areas that require attention and improvement before December 2023.
A recent report by YCP Solidiance, an international consulting firm, highlights the ESG risks faced by most Southeast Asian nations as the recovery from the pandemic slows down. According to the report, Singapore stands out as the only country in the region with a low ESG risk score of 34.9.
On the other hand, Cambodia, Myanmar, and Laos have higher ESG risk scores of over 50, particularly concerning their sustainability targets. Thailand falls in the medium-risk category, scoring 43.1. In response, the Thai Energy Ministry has set ambitious goals, aiming to increase the share of renewable energy to 50% of the electricity generation mix by 2050, achieve carbon neutrality by 2050, and attain net-zero greenhouse gas (GHG) emissions by 2065.
The report underscores the substantial and interconnected ESG challenges confronted by the region, highlighting the importance of government coordination and alignment among Southeast Asian countries. Additionally, the survey conducted by YCP brings to light that companies in Southeast Asia are still trailing their American and European counterparts when it comes to achieving emissions reduction targets and reducing the percentage of carbon dioxide emissions.
Furthermore, the report reveals that only Malaysia (68.8%), Thailand (66%), and Singapore (58.6%) have a notable proportion of companies implementing environmental supply chain policies.
A joint report by Bain & Company, Temasek, and Microsoft in 2022 revealed that non-renewable sources contribute to 85% of the region's primary energy supply, while carbon dioxide emissions are projected to increase by up to 2,400 tonnes by 2040.
Despite governments in Southeast Asia committing to achieve net-zero emissions between 2050 and 2065 following the Paris Agreement, a 2022 UN report states that the region is not on track to meet any of the 17 sustainable development goal targets by 2030. The report attributes this shortfall to higher income levels in the region, leading to increased material footprint and consumption. Unsustainable consumption patterns have resulted in a significant rise in GHG emissions, exacerbating the climate emergency in the area.
Engaging with ESG has been a transformative journey for me over the past few years. It has surpassed being a mere commercial venture and become a powerful avenue for delivering positive change to both organizations and society at large, no matter how small my contributions may seem. Unlike my experiences with other businesses, where my impact was limited to the company level, ESG has opened doors to create meaningful change on a broader scale.
What truly fuels my passion for ESG is its ability to foster collaboration among like-minded individuals. While we may be driven by different motivations, we are united by a shared desire to contribute to real change and ensure a more sustainable world, especially considering the diminishing natural resources we face. This collaboration transcends boundaries, bringing together diverse perspectives and expertise to address complex challenges collectively.
The growing acceptance of ESG as a necessity in today's world is undeniable. It has moved beyond being perceived as a passing trend and has become an integral part of responsible business practices. As an ESG professional, I take pride in being part of a movement that is actively making a difference for present and future generations. We understand the urgency of addressing environmental challenges, promoting social equality, and ensuring good governance practices to create a sustainable future.
In conclusion, my involvement in ESG has transcended commercial motives and evolved into a commitment to delivering positive change. ESG provides a platform for collaboration, bringing together individuals with different perspectives and motivations to collectively work towards a more sustainable world. It not only benefits the planet and society but also offers tangible advantages for organizations. As an ESG professional, I am honored to contribute to this movement, knowing that our actions today will shape a brighter future for generations to come.
I have been asked many times in the last six months to relate my AI Technology build experience (Wipro Technologies, Ai-XPRT and now at ESG Disclose) to automate the assurance of unstructured data using Artificial Intelligence and to share the best practice with our wider community. I am not a programmer nor a mathematician but here is the very iterative process we took to build AI enabled assurance platforms.
1. First, gather all relevant data sources that will be needed for the AI automation process. This could include databases, CSV files, or web scraping tools to collect data from online sources.
2. Next, perform initial data cleaning and preprocessing to ensure that the data is in a usable format. This could involve removing any duplicates, missing values, or outliers in the data.
3. Once the data is cleaned, it is important to perform data exploration and visualization to gain a better understanding of the data and identify any potential patterns or trends. This will help inform the next steps in the process.
4. Based on the results of the data exploration, select the appropriate machine learning algorithms and techniques to use in the AI automation process. This could involve using supervised or unsupervised learning techniques, depending on the nature of the data and the desired outcome.
5. Split the data into training and testing sets in order to evaluate the performance of the machine learning model. It is important to use a representative sample of the data for this process in order to ensure accurate results.
6. Train the machine learning model using the training data, and evaluate its performance using the testing data. If the model is not performing well, consider adjusting the algorithms or techniques used, or adding additional data to the training set.
7. Finally, once the machine learning model is performing well on the testing data, it can be deployed for use in the AI automation process. It is important to continuously monitor and evaluate the performance of the model to ensure that it is accurately predicting outcomes and providing value to the business.
In conclusion, this is very much an iterative process and requires to be constantly monitored to improve the understanding through the refinement of the models. I hope this helps inform organisations and individuals when considering automating data assurance
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