· 9 min read
As we navigate this important decade for sustainable development and climate action, the UAE consensus to triple renewable energy capacity and double energy efficiency improvements by 2030 sets a clear agenda. The Pact for the Future, presented at the Summit of the Future, will further guide us towards these targets. Meeting these bold commitments demands bold actions, and achieving them will be challenging without leveraging the triptych of digital innovation, data, and AI.
These are powerful tools to speed up the energy transition, helping to identify the best investment opportunities, guiding smart decision-making, and driving breakthroughs in renewable energy. In fact, if scaled effectively, digital technologies could reduce emissions by 20% by 2050 in the energy, materials, and mobility sectors — the three largest contributors to global emissions. In other words, digital innovation could be the ‘low-hanging fruit’ that can offer a fast, cost-effective way to accelerate the energy transition.
But creating digital tools is only part of the solution; we need to ensure that data-driven policies translate into real-world impact. The energy transition value chain is producing a vast amount of data, which must be leveraged to respond to the socio-economic implications and environmental challenges of the transition. The true value of data lies in how well we use them to reveal integrated opportunities for sustainable energy development and ensure a just energy transition. However, without robust regulatory and institutional frameworks, these changes cannot be fully realized. Read on to explore how we can achieve this.
With energy demand expected to rise 18% by 2050, how can adopting digital technologies across the energy value chain accelerate and economize the energy transition?
Digital technologies, from AI to 3D printed sensors, are transforming the energy sector by enhancing efficiency and expanding access to renewable sources. With energy demand projected to increase by 18% through 2050 and energy management inefficiency at 60%, digitalization offers immense potential to optimize the entire energy value chain, from renewable energy production and storage to more efficient distribution and consumption.
Digital tools and geospatial data have a significant impact by identifying optimal investment opportunities and enabling informed decision-making. Tools like the GeoHub, Electricity Access Forecasting AI and Global Electrification Platform (GEP) 3.0 exemplify this, providing a centralized ecosystem of geospatial services, large-scale forecasts and simplifying least-cost electrification planning, respectively. Moreover, these tools help in determining areas covered by the national grid and those needing decentralized systems. For example, Kenya is utilizing digital tools such as the Energy Access Explorer to support integrated energy planning in regions like Kitui and Narok counties. Through these and similar efforts driven by the strategic implementation of the National Electrification Strategy, Kenya has seen a rapid increase in electricity access, rising from 20% in 2013 to nearly 75% in 2021.
For areas with resource constraints and less advanced infrastructure, digital innovation presents a lucrative opportunity to leapfrog traditional energy systems, enabling more efficient, cost-effective energy solutions that can drive sustainable development. Through advanced analytics and predictive capabilities, machine learning algorithms can analyze data volumes equivalent to millions of gigabytes from smart grids, weather patterns, and consumption habits to optimize energy distribution and predict demand with high accuracy. While this has already increased productivity by 20% and reduced costs by 10%, primarily in the industrial sector in developed economies, the real disruptive potential lies in developing regions. For example, estimates suggest that India could save INR 9.5 trillion by upgrading 250 million conventional meters to smart meters. As digital technologies continue to evolve and become more accessible, they are set to optimize energy systems, improve access as well as free resources for healthcare, education, and infrastructure, triggering a positive cascade effect across multiple sustainable development goals.
The future of the energy transition lies not just in the deployment of technology but in ensuring that the benefits of these advancements are shared equitably
The success of digital interventions in the energy sector depends on integrating social, environmental, and developmental dimensions into the data value chain. Technology-first approaches often risk overlooking how energy systems interact with communities and can fail to capture how these innovations can uplift lives. Indicators, including but not limited to, income distribution, employment rates, gender and community impacts, provide a critical context for understanding how energy policies and practices affect different segments of society. Therefore, by combining these aspects with traditional energy metrics, policymakers can better assess the implications of energy transitions on local economies and social equity. The Clean Energy Equity Index highlights this approach, bringing together geospatial analytics with environmental, economic, and social factors to provide a comprehensive assessment.
This is particularly relevant in the context of the energy transition, where job creation and equitable opportunities must go hand in hand with technological innovation. While the green transition is projected to create over 100 million jobs by 2030, addressing the retraining needs of the estimated 78 million jobs lost is crucial. In South Africa, for example, where around 90,000 coal-dependent jobs are at risk, the lack of retraining programs in regions like Mpumalanga can affect workers. In response to this challenge, AI-driven personalized training programs could offer tailored reskilling pathways to workers from traditional energy sectors, helping them to transition into renewable roles, creating new jobs and ensuring that communities benefit from both innovation and economic inclusion.
Moreover, as we scale digitalization for energy efficiency, its deployment introduces new risks such as data bias, ethical concerns, and transparency issues that demand careful governance. AI models, for instance, rely on data quality. Biased or incomplete data can lead to poor decision-making and reinforce existing inequities, such as prioritizing urban energy needs over rural ones. Further, the black-box nature of many data systems creates challenges in transparency, making it difficult for stakeholders to understand the recommendations. To address these risks, governance frameworks are needed to ensure transparency, accountability, and fairness in digital applications. Such frameworks must include ethical guidelines and participatory models to build trust and ensure equitable outcomes.
Building local expertise and infrastructure is key to scaling data-driven energy solutions in developing regions
Capacity building and collaboration among governments, private sectors, and educational institutions can bridge the data gaps and ensure all regions benefit from digital advancements. Data collection frameworks and open data repositories are proving to help advance digitalization and AI in sustainable energy, especially in developing countries, where there is further potential to develop and strengthen the data infrastructure. Initiatives such as the World Bank Open Data tool and UNDP Data Futures Exchange Platform support this agenda by providing an integrated repository of diverse datasets, making data-driven decision-making feasible.
Moreover, partnerships between international organizations and local governments for training programs are building local expertise in data collection and AI usage for sustainable energy. For example, the AI for Development (AI4D) Africa program, focuses on building AI talent and capacity across African countries by funding research, training, and innovation hubs. Additionally, projects such as the UNDP trilateral initiative with China and Ethiopia in Sri Lanka, which established an Energy Data Management System, show how international cooperation can effectively improve energy data collection. Programs like these can facilitate local skill development, knowledge exchange and innovation and foster regional collaboration on energy projects, ensuring that solutions are context-specific and sustainable. Establishing regional centers and replicating successful models can further accelerate the deployment of such initiatives in more countries.
However, the rising energy consumption of AI presents a significant concern to global sustainability, making it crucial to adopt Green AI solutions that prioritize energy efficiency and emission reduction. Building scalable and equitable data-driven and AI solutions for sustainable energy requires robust technical infrastructure. This includes cloud platforms and decentralized data storage systems that reduce costs, minimize latency, and enhance accessibility for developing countries. On the flip side, AI systems, particularly generative AI models, are energy-intensive; training large models can emit as much carbon as several cars over their lifetimes. To mitigate this, Green AI focuses on optimizing algorithms, reducing computational needs, and using renewable energy. For regions that still are in the early stages of establishing data centers, this presents an opportunity to invest in renewable energy sources to power these centers reliably and sustainably.
Investment in digital tech for energy is gaining momentum: with digital grid investments up over 50% since 2015, what strategies will sustain and accelerate this growth?
Significant policy shifts are required to unlock the potential of digitalization and AI in sustainable energy by encouraging innovation, data sharing, and ethical usage. Current energy policies often lack clarity on data and AI, creating barriers for developers. Governments play a key role by establishing clear guidelines on AI ethics, data privacy, and cybersecurity to build trust and encourage adoption. Take, for instance, the policies promoting open access to energy data. They are crucial for transparency and inclusivity in digital and AI applications. Similarly, revising regulations to mandate data sharing between utilities and tech companies can accelerate these tools for grid management and renewable integration. An environment conducive to innovation and strategic partnerships can attract investments and advance digitalization in sustainable energy solutions. This involves creating innovation hubs, research centers, and incubators that connect tech firms, energy providers, academia, and government agencies to collaborate on energy projects. Governments can promote this by implementing policies that reduce bureaucratic obstacles and facilitate digitalization and clean energy R&D. Additionally, incentives like tax breaks, grants, and streamlined approvals can drive private sector involvement and public-private partnerships, fast-tracking the creation of a data and AI ecosystem in the energy sector.
Diversifying funding can help scale digital and AI-driven energy solutions, de-risk investments, and ensure their benefits reach underserved regions. Traditional financing alone may not support large-scale digital deployments in sustainable energy. Blended finance, combining public, private, and philanthropic funding, offers a more effective approach by de-risking investments and attracting private capital. Parallelly, impact investing, which seeks financial returns and social or environmental impact along with microfinance and community-based models can fund digital applications like optimizing off-grid solar solutions in developing regions. In addition, programs like the European Union’s Horizon Europe, which provide funding to collaborative AI and clean energy projects, can further support the scaling of solutions across borders.
This article is also published on the UNDP. illuminem Voices is a democratic space presenting the thoughts and opinions of leading Sustainability & Energy writers, their opinions do not necessarily represent those of illuminem.
