In Taichung, Dasyueshan National Forest Recreation Area (大雪山國家森林)offers a range of valuable services, including water regulation, biodiversity conservation, carbon sequestration, and recreational opportunities. The forests help regulate water flows by capturing rainfall and releasing it slowly, which is crucial for reducing flood risks in the city and maintaining a steady supply of water during drier seasons. This water regulation service supports agriculture, urban needs, and the region’s resilience against extreme weather events.

Ecological capital: examples

Additionally, Taichung’s forests are home to diverse flora and fauna, including endemic species like the Formosan landlocked salmon and various bird species, which contribute to Taiwan’s rich biodiversity. These forests act as carbon sinks, sequestering carbon dioxide and thus helping Taiwan meet its climate change mitigation goals. The natural beauty of these forested areas also attracts eco-tourism, providing economic benefits to local communities and raising awareness about environmental conservation.

Ecological capital: examples

The recognition of these forest ecosystems as ecological capital emphasizes their value not just in terms of biodiversity and climate regulation but also for the broader socioeconomic benefits they provide to Taichung and its residents. Sustainable management of these forests is crucial to ensure that these ecosystem services continue to support the city’s environmental health and resilience.

Ecological capital: examples

Motivation

Motivation

• Preparing the next generation of leaders with AI literacy

  • Initiatives like aiEDU's AI Readiness Framework to equip students and educators with the skills needed to navigate an AI-driven world.

• Emphasizing AI literacy and readiness, focusing on understanding AI's interdisciplinary impacts and fostering collaboration and creativity alongside technology critical in developing leaders who can apply AI insights to sustainable governance challenges.

   

Motivation

• AI-forward governance involves adopting policies that ensure ethical and effective use of AI in managing ecological capital. UNESCO's Global AI Ethics and Governance Observatory's AI Readiness Methodology and the Indonesia example serve as a model for how countries can evaluate and enhance their AI governance frameworks.

• UT System proposes an AI-Forward - AI-Responsible Initiative to provide guidance to use AI in secure and responsible (ethical and legal) manner. 

Motivation

• Sources
  [1] aiEDU: The AI Education Project unveils AI Readiness Framework to ... https://www.prnewswire.com/news-releases/aiedu-the-ai-education-project-unveils-ai-readiness-framework-to-help-students-teachers-school-systems-prepare-for-the-transformation-driven-by-ai-302270994.html
  [2] [PDF] 432 THE READINESS TO USE AI IN TEACHING SCIENCE ... - ERIC https://files.eric.ed.gov/fulltext/EJ1429748.pdf
  [3] Preparing National Research Ecosystems for AI: Strategies and ... https://council.science/publications/ai-science-systems/
  [4] The AI Revolution: Awareness of and Readiness for AI-based Digital ... https://jrbe.nbea.org/index.php/jrbe/article/view/106
  [5] UNESCO and KOMINFO Completed AI Readiness Assessment https://www.unesco.org/en/articles/unesco-and-kominfo-completed-ai-readiness-assessment-indonesia-ready-ai
  [6] Mapping the Worlds Readiness for Artificial Intelligence Shows ... https://www.imf.org/en/Blogs/Articles/2024/06/25/mapping-the-worlds-readiness-for-artificial-intelligence-shows-prospects-diverge
  [7] AI Readiness in Higher Education - GovTech https://papers.govtech.com/AI-Readiness-in-Higher-Education-143078.html
  [8] Empowering educators to be AI-ready - ScienceDirect.com https://www.sciencedirect.com/science/article/pii/S2666920X22000315

Why Governance of EC needs AI?

The governance of ecological capital presents multi-faceted challenges that stem from both direct and indirect human activities, as well as the complexities inherent in managing natural resources sustainably.

1. Depletion and Biodiversity Loss

2. Economic and Regulatory Pressures

3. Data and Methodological Gaps

4. Corporate Governance Challenges

5. Intrinsic Value Recognition

Motivation

Depletion and Biodiversity Loss

Natural capital is under severe threat due to factors such as land use changes, overexploitation of resources, invasive species, climate change, and pollution. These threats lead to habitat destruction, species migration, and increased extinction risks, with approximately one million species currently at risk of extinction (Purvis 2019). This depletion results in significant economic impacts, including a projected decline in global GDP of $2.7 trillion annually by 2030 (Niles et al 2022).

Motivation

Economic and Regulatory Pressures:

Businesses face challenges related to increased raw material costs and supply chain disruptions due to natural capital depletion. Stricter ecological regulations necessitate more sustainable operations, raising compliance costs and posing reputational risks if businesses are seen as contributors to environmental degradation. Additionally, the lack of external incentives for undertaking natural capital projects complicates efforts to integrate these considerations into business strategies.

Motivation

Data and Methodological Gaps

There is a lack of trusted data and methodologies for quantifying the use and impact of natural capital. This gap hinders the ability to make informed decisions and assess the benefits of ecosystem services accurately. Furthermore, the complexity of ecosystem degradation, with its multiple causes and effects, makes it challenging to translate ecological resilience into actionable policy measures

Motivation

Corporate Governance Challenges

Companies struggle with integrating sustainability into corporate governance due to knowledge gaps among directors regarding sustainability issues. This gap limits their ability to make informed decisions that balance corporate interests with societal and environmental needs. The need for expertise in sustainability is crucial to fulfilling fiduciary duties and avoiding potential litigation risks.

Motivation

Intrinsic Value Recognition

The commodification of natural capital raises concerns about recognizing the intrinsic value of biodiversity and a healthy environment. While economic valuation can aid in policy integration, it often fails to capture the full cultural and spiritual significance of nature.

Motivation

Why AI readiness is essential?

• Data Management and Analysis

• Enhanced Decision-Making

• Addressing ESG Challenge

• Risk Management

• Infrastructure Development

Motivation

Data Management and Analysis:

AI facilitates the processing and analysis of large datasets, which is crucial for understanding and managing ecological systems. For instance, AI can optimize resource use and enhance productivity in sectors like agriculture, water, energy, and transport, potentially boosting global GDP by 3.1–4.4% while reducing greenhouse gas emissions by 1.5–4.0% by 2030 (Herweijer et al. undated)

Motivation

Enhanced Decision-Making

AI supports informed decision-making by providing accurate predictions and recommendations, helping policymakers develop effective strategies for climate change mitigation and resource management. This capability is vital for sustainable governance as it allows for more precise and timely interventions.

Motivation

Addressing ESG Challenges

The integration of AI into Environmental, Social, and Governance (ESG) practices can enhance corporate governance by analyzing data to assess performance and identify improvements. This alignment is crucial as businesses face increasing pressure from stakeholders to demonstrate transparency and sustainability.

Motivation

Risk Management

AI can help identify and mitigate risks associated with ecological governance, such as environmental degradation or resource depletion. A harmonized regulatory approach is necessary to ensure that AI applications are accountable and ethical, preventing potential negative impacts like bias or privacy infringements.

Motivation

Infrastructure Development

Establishing a robust data ecosystem is essential for AI readiness. Organizations must develop scalable data management systems to handle the increasing volume and complexity of data required for AI applications in ecological governance.

AI-Readiness

FAIR

The FAIR data principles are a set of guidelines designed to enhance the management and stewardship of scientific data, ensuring that they are Findable, Accessible, Interoperable, and Reusable. These principles were first published in 2016 to address the increasing reliance on computational systems for data handling due to the growing volume and complexity of data.

CARE

• The CARE Principles for Indigenous Data Governance, developed by the Research Data Alliance International Indigenous Data Sovereignty Interest Group, are designed to ensure that data practices respect the rights and interests of Indigenous communities. These principles are particularly relevant in AI-driven ecological research, where data involving Indigenous lands and resources are often utilized. Here’s a breakdown of the CARE Principles:
  • Collective Benefit
  • Authority to Control
  • Responsibility
  • Ethics

Key Competencies for AI Literacy

 Understanding AI

Ethical AI 

Interdisciplinary Collaboration

Critical Thinking and Evaluation

Communicating AI

Policy on using generative AI in class

There are generally three policies on governing using generative AI in course setup:

1. Prohibiting AI Use

2. Allowing AI Use Without Restrictions

3. Permitting AI Use with Disclosure

1. Michigan
   
   "In principle you may submit AI-generated code, or code that is based on or derived from AI-generated code, as long as this use is properly documented in the comments: you need to include the prompt and the significant parts of the response. AI tools may help you avoid syntax errors, but there is no guarantee that the generated code is correct. It is your responsibility to identify errors in program logic through comprehensive, documented testing. Moreover, generated code, even if syntactically correct, may have significant scope for improvement, in particular regarding separation of concerns and avoiding repetitions. The submission itself should meet our standards of attribution and validation.

Policy on using generative AI in class

2. Harvard

Policy on using generative AI in class

3. Carnegie Mellon University

Policy on using generative AI in class

4. UTD (some courses)

Policy on using generative AI in class

Using generative AI may not save time, but will improve quality and deepen thought process.

Policy on using generative AI in class

The design of this study is composed of three elements:

• Big data

• Generative Pre-trained Transformer (GPT) models

• Predictive Scenario Analysis

It begins with the collection of diverse datasets to build text data corpora, followed by extensive data preprocessing and feature engineering and predictive models with scenario analysis.

Illustration: GPT-based prediction

The use of GPT models facilitates a deep contextual understanding of public sentiments and perspectives. In parallel, generative data methods, particularly Generative Adversarial Networks (GANs), are employed for scenario analysis.

GPT models

Generative data methods generate synthetic data representing various hypothetical scenarios, enabling the exploration of how public opinion might shift in response to different policy changes. Sentiment analysis and opinion mining are then applied to gauge public support or opposition and to identify specific policy aspects that drive public opinion.

Synthetic data

Most recent studies demonstrate GPT can be used to generate "virtual populations" using "silicon sampling" to simulate targeted human responses (Argyle et al. 2023). 

Synthetic data

Most recent studies demonstrate GPT can be used to generate "virtual populations" using "silicon sampling" to simulate targeted human responses (Argyle et al. 2023). 

Synthetic data

Most recent studies demonstrate GPT can be used to generate "virtual populations" using "silicon sampling" to simulate targeted human responses (Argyle et al. 2023). 

Synthetic data

The study also proposes including predictive modeling and scenario forecasting, using the developed models to predict future trends in public opinion and to forecast how these might evolve under different policy scenarios. This approach not only offers a comprehensive understanding of current public sentiments but also equips policymakers with tools to align policies more closely with public preferences and concerns, thereby enhancing the effectiveness of environmental policy-making.

Predictive models and scenario forecasting

The following outlines the steps in building a GPT-based policy advising system to more accurately integrate public opinion in policy making:

1. Data collection: Corpora building

2. Data Preprocessing and Feature Engineering

3. Application of GPT for Contextual Analysis

4. Sentiment Analysis and Opinion Mining

5. Generative Data Modeling for Scenario Analysis

Steps

Gather large datasets of public opinion data related to CO2 management and energy policies, including:

• Social media posts

• Survey responses

• Forum discussions

  • PTT

  • DCard

• News comments (e.g. editorials)

Step 1: Corpora building

• Preprocess the data for NLP tasks:

  • Perform feature engineering to identify key variables influencing public opinion, such as:

    • sentiments

    • slow onset events and extreme weather events

    • frequency of policy mentions

    • other factors such as demographics

• Utilize NLP techniques to extract and construct meaningful features from unstructured data.

Step 2: Data Preprocessing and Feature Engineering

• Use GPT-based models for deep contextual analysis of public opinions, understanding underlying sentiments and perspectives.

• Build context for conditioning in silicon sampling

Step 3: Application of GPT for Contextual Analysis

• Conduct temporal sentiment analysis to gauge public support or opposition over time.

• Use opinion mining to identify specific policy aspects that are most influential in public opinion.

Step 4:  Sentiment Analysis and Opinion Mining:

• Employ generative models like GANs to create synthetic data representing various hypothetical public opinion scenarios under different policy conditions especially in contexts with limited data (few-shot learning).

• Analyze how public opinion might shift in response to changes in CO2 management and energy policies.

Step 5: Generative Data Modeling for Scenario Analysis

• This proposal will lay out future roadmap to achieve comprehensive understanding of public sentiments and opinion movements

• Challenges:

  • Fine-tuning

    • High computational cost for model

    • Spurious features of training data (e.g. fake news, biased surveys)

Expected Outcomes and Challenges

• Project Significance:  

  • New technology in AI allows better understanding public opinion via GPT and predictive scenario analysis
    

  • It will significantly enhance environmental policy-making

  • It helps stake-holders align policies with true, real-time public preferences.

Conclusion 

• Q: Why not just use surveys?

• A: Surveys are snapshots of public opinion, which could be subject to change over time and different scenarios (e.g. ECFA).  Using GPT-based methods will scale and improve overtime, giving consistent metrics for evaluating biases and stable vs. precarious public opinions.

• Q: Can A.I. methods be absolutely correct?

• A: There is always a part of public opinion we can never fully measure or understand.  With AI assistance, we will learn about this sub-population better and better over time. 

Q & A

1. AI-driven Data Collection

2. Data Preprocessing and Feature Engineering

3. Application of GPT for Contextual Analysis

4. Generative Data Modeling for Scenario Analysis

5. Sentiment Analysis and Opinion Mining

6. Predictive Modeling and Scenario Forecasting

7. Interactive Dashboard and Visualization

Directions

What is Generative AI (GenAI)?

Generative AI is a subset of artificial intelligence that focuses on creating new content such as text, images, videos, and audio in response to user prompts. This technology leverages sophisticated machine learning models, particularly deep learning models, to simulate human-like creativity and decision-making processes by identifying patterns in large datasets (Amazon Q, IBM, Lawton) .

What is Generative AI (GenAI)?

History

• Generative AI's roots trace back to the 1960s with early chatbots.

• Significant advancements came in 2014 with the development of Generative Adversarial Networks (GANs), which enabled the creation of realistic images and audio.

• Introduction of transformer-based models in 2017 further propelled generative AI's capabilities, leading to the creation of large language models like GPT-3, which can generate coherent text based on minimal input.

What is Generative AI (GenAI)?

Applications

Generative AI is widely used across various fields:

• Content Creation: Tools like ChatGPT and DALL-E generate text and images, respectively, aiding in creative writing, graphic design, and marketing.

• Software Development: AI models assist in coding by generating code snippets or translating between programming languages.

• Healthcare: In drug discovery, generative AI helps design new molecular structures.

• Entertainment: It is used for creating virtual simulations and special effects in video games and movies.

What is Generative AI (GenAI)?

Future Developments

• Advancements in multimodal models that can process and generate multiple types of content simultaneously.
• Personalized content creation and more sophisticated virtual assistants.
• User interfaces more accessible to non-experts, expanding use across industries.

What is Generative AI (GenAI)?