AI and ESG | An Introductory Guide for ESG Practitioners
The growing use of AI, in all its forms, is permeating all organisations and all activities. AI is helping to solve some of our most pressing challenges in areas such as health, climate change, sustainability, accessibility and inclusion. It is also posing significant new risks and challenges. There is a meaningful overlap between AI and the work you do in ESG. We’ve designed this practical guide to support you in understanding AI as part of your ESG role.
AI and ESG | An Introductory Guide for ESG Practitioners
The growing use of AI, in all its forms, is permeating all organisations and all activities. AI is helping to solve some of our most pressing challenges in areas such as health, climate change, sustainability, accessibility and inclusion. It is also posing significant new risks and challenges. There is a meaningful overlap between AI and the work you do in ESG. We’ve designed this practical guide to support you in understanding AI as part of your ESG role.
Artificial Intelligence in Manufacturing
For over a decade, Artificial Intelligence (AI) technologies and applications are proliferating in a rapid pace. The rise of AI is driven by a variety of factors including the unprecedented improvements in hardware and software, and the explosion in the amount of generated data. These advances enable the development of sophisticated AI models (e.g., deep learning models, deep reinforcement learning models, large language models), as well as their deployment and execution in realistic settings. This is also the reason why modern manufacturers are undertaking significant investments in AI solutions as part of their digital transformation journey. As a result, AI is rapidly transforming the manufacturing industry, through enabling tangible improvements in the efficiency, quality, and productivity of industrial organizations.
Artificial Intelligence in Manufacturing
For over a decade, Artificial Intelligence (AI) technologies and applications are proliferating in a rapid pace. The rise of AI is driven by a variety of factors including the unprecedented improvements in hardware and software, and the explosion in the amount of generated data. These advances enable the development of sophisticated AI models (e.g., deep learning models, deep reinforcement learning models, large language models), as well as their deployment and execution in realistic settings. This is also the reason why modern manufacturers are undertaking significant investments in AI solutions as part of their digital transformation journey. As a result, AI is rapidly transforming the manufacturing industry, through enabling tangible improvements in the efficiency, quality, and productivity of industrial organizations.
Green Industrial Policies for the Net-Zero Transition
Countries are increasingly turning to industrial policy to address concerns over climate change, energy security and strategic autonomy. This paper explores the potential and risks of green industrial policies with a focus on green subsidies, and illustrates some key design considerations that governments should take into account when providing such support. To get green industrial policy right, governments need to ensure that support is targeted, time-bound, and accompanied by effective monitoring and evaluation.
Green Industrial Policies for the Net-Zero Transition
Countries are increasingly turning to industrial policy to address concerns over climate change, energy security and strategic autonomy. This paper explores the potential and risks of green industrial policies with a focus on green subsidies, and illustrates some key design considerations that governments should take into account when providing such support. To get green industrial policy right, governments need to ensure that support is targeted, time-bound, and accompanied by effective monitoring and evaluation.
Assessing Global Water Megatrends
Abstract Currently some 2.5–3.0 billion people do not have access to clean water. To ensure all these people and an additional 2.3 billion people expected by 2050 have access to adequate quantity and quality of water for all their needs will be a very challenging task. Future water-related problems and their solutions will be very different from the past. Identification and solutions of these problems will require new insights, knowledge, technology, management and administrative skills, and effective coordination of multisectoral and multidisciplinary skills, use of
innovative approaches, adaptable mindsets and proactive functional institutions. Many of the existing and widely accepted paradigms have to be replaced in the future turbulent and complex era of widespread social, economic, cultural and political changes. The new paradigms must accommodate diversified and contradictory demands of different stakeholders and their changing economic, social and political agendas. Rapidly changing global conditions will make future water governance more complex than ever before in human history.
Assessing Global Water Megatrends
Abstract Currently some 2.5–3.0 billion people do not have access to clean water. To ensure all these people and an additional 2.3 billion people expected by 2050 have access to adequate quantity and quality of water for all their needs will be a very challenging task. Future water-related problems and their solutions will be very different from the past. Identification and solutions of these problems will require new insights, knowledge, technology, management and administrative skills, and effective coordination of multisectoral and multidisciplinary skills, use of
innovative approaches, adaptable mindsets and proactive functional institutions. Many of the existing and widely accepted paradigms have to be replaced in the future turbulent and complex era of widespread social, economic, cultural and political changes. The new paradigms must accommodate diversified and contradictory demands of different stakeholders and their changing economic, social and political agendas. Rapidly changing global conditions will make future water governance more complex than ever before in human history.
From Digital Twin Paradigm to Digital Water Services
ABSTRACT
In the context of water distribution networks (WDNs), researchers and technicians are actively working on new ways to transition into the digital era. They are focusing on creating standardized methods that fit the unique characteristics of these systems, with a strong emphasis on developing customized digital twins. This involves combining advanced hydraulic modeling with advanced data-driven techniques like artificial intelligence, machine learning, and deep learning. This paper begins by giving a detailed overview of the important progress that has led
to this digital transformation. It highlights the potential to create interconnected digital water services (DWSs) that can support all aspects of managing, planning, and designing WDNs. This approach introduces standardized procedures that allow a continuous improvement of the digital representation of these networks. Additionally, technicians benefit from DWSs developed as QGIS software plugins. These services strategically enhance their understanding of technical decisions, improving logical reasoning, consistency, scalability, integrability, efficiency,
effectiveness, and adaptability for both short-term and long-term management tasks. Notably, the framework remains adaptable, ready to embrace upcoming technological advancements and data gathering capabilities, all while keeping end-users central in shaping these technical developments.
From Digital Twin Paradigm to Digital Water Services
ABSTRACT
In the context of water distribution networks (WDNs), researchers and technicians are actively working on new ways to transition into the digital era. They are focusing on creating standardized methods that fit the unique characteristics of these systems, with a strong emphasis on developing customized digital twins. This involves combining advanced hydraulic modeling with advanced data-driven techniques like artificial intelligence, machine learning, and deep learning. This paper begins by giving a detailed overview of the important progress that has led
to this digital transformation. It highlights the potential to create interconnected digital water services (DWSs) that can support all aspects of managing, planning, and designing WDNs. This approach introduces standardized procedures that allow a continuous improvement of the digital representation of these networks. Additionally, technicians benefit from DWSs developed as QGIS software plugins. These services strategically enhance their understanding of technical decisions, improving logical reasoning, consistency, scalability, integrability, efficiency,
effectiveness, and adaptability for both short-term and long-term management tasks. Notably, the framework remains adaptable, ready to embrace upcoming technological advancements and data gathering capabilities, all while keeping end-users central in shaping these technical developments.
