AI for Climate

Action Applications and Principles in Development Cooperation

As the climate crisis demands global cooperation and continuous innovation, AI and Machine Learning (ML) present themselves as powerful tools to accelerate effective solutions on a global scale. The aim of this paper is to explore the intersection of AI and climate change (CC) from an international development perspective, with a focus on sustainability and leaving no one behind (inclusion). Through a feminist climate policy perspective, the German government seeks to accelerate a “Just Transition” in which economies are environmentally and climatologically sound, equitable and climate friendly.
Over the last few years, GIZ has increasingly used AI in collaboration with partner countries, with over 20 AIbased pilots developed worldwide. As stated by Ms. Ingrid-Gabriela Hoven, Managing Director of GIZ,
“The role of development cooperation is key – as a convener and honest broker for equal rights, as an ambassador for addressing the specific challenges for women and girls, as an advisor for regulatory frameworks and local innovations, as a capacity builder for systems and enablers and awareness raiser
for the risks and potentials.” GIZ has already begun implementing AI to advance climate mitigation and adaptation goals across a variety of sectors including agriculture and forestry, energy, transportation, waste management, manufacturing /industry, infrastructure, and removals.

Pilots have included:

Forestry conservation monitoring and sustainable land use planning;

Crop yield forecasting and crop disease detection for precision agriculture;

Early warning systems for natural disasters; Solar PV viability assessments;

Forecasts of energy data for wind and PV systems;

Predictive maintenance for energy infrastructure;

Supply chain transparency for sustainable forestry and mining; Broad-scale
analysis of climate policy documentation;

Climate risk assessment of infrastructure projects; and Urban transportation planning. Furthermore, through the FAIR Forward initiative, GIZ is supporting a people-centered, climate-friendly, and inclusive approach to digitalization. In addition to driving innovation, a key component of FAIR Forward’s work includes the development of guidelines and policy frameworks for ethical AI, data protection and privacy. This paper will contextualize FAIR Forward’s guiding principles on AI adoption through the lens of applications for climate action. The paper will start by reviewing the status quo of climate applications for AI across sectors and in the context of international cooperation, and then discuss guiding principles that will be crucial for GIZ and other organizations to address when implementing such applications.
AI is largely characterized by its ability to analyze data, recognize patterns, and inform decision-making. This has immense potential to fundamentally change the way we understand and deal with climate change issues. AI can help us make more accurate predictions about future climate developments and better understand the impact of certain climate change mitigation and adaptation measures. By using ML algorithms, for example, complex climate models can be created that can simulate different
scenarios and predict the potential impact of political decisions or technological innovations on the climate. Furthermore, AI continues to make a crucial contribution to the fight against climate change by optimizing resource utilization and energy efficiency. AI systems are becoming increasingly advanced in pattern recognition, which is being used across key industries to improve the sustainability of business operations. Companies, consumers, entrepreneurs, and entire states can all use AI systems to make more environmentally and climate-friendly decisions and optimize processes for efficient resource and energy use.
While AI systems are being used to identify solutions for energy savings, one of the important critiques of current AI systems is the significant energy consumed in training these systems. There are also important social and ethical aspects to consider when adopting AI applications. For example, due to existing biases in training data, there have been numerous cases where AI systems have been shown to reproduce and even exacerbate social inequalities and discriminatory patterns in their generated results.
This is particularly concerning for women and marginalized communities, as it can widen existing disparities and perpetuate systemic injustices. Because AI solutions are only as good as the data that they have been trained with, there is a risk of biased, unethical, and/or inaccurate results. Further to the risk of biased or incomplete underlying datasets, AI models and algorithms can also have bias introduced through the data labelling, model training, and deployment phases. It is therefore crucial that ethical principles and standards are developed and adhered to when developing and implementing AI solutions, including in the field of climate change. This requires careful monitoring, transparency, and governance
to ensure that AI-based approaches to tackling climate.

source :

https://www.linkedin.com/posts/umair-khan-01a583268_ai-for-climate-action-activity-7254344135376048129-EDay?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAtGGkQBsxwMBmX3lEJO8btihnfBCaHqTz4