The recent advancement in data science coupled with the revolution in digital and satellite technology has improved the potential for artificial intelligence (AI) applications in the forestry and wildlife sectors. India shares 7% of global forest cover and is the 8th most biodiverse region in the world. However, rapid expansion of developmental projects, agriculture, and urban areas threaten the country’s rich biodiversity. Therefore, the adoption of new technologies like AI in Indian forests and biodiversity sectors can help in effective monitoring, management, and conservation of biodiversity and forest resources. We conducted a systematic search of literature related to the application of artificial intelligence (AI) and machine learning algorithms (ML) in the forestry sector and biodiversity conservation across globe and in India (using ISI Web of Science and Google Scholar). Additionally, we also collected data on AI-based startups and non-profits in forest and wildlife sectors to understand the growth and adoption of AI technology in biodiversity conservation, forest management, and related services. Here, we first provide a global overview of AI research and application in forestry and biodiversity conservation. Next, we discuss adoption challenges of AI technologies in the Indian forestry and biodiversity sectors. Overall, we find that adoption of AI technology in Indian forestry and biodiversity sectors has been slow compared to developed, and to other developing countries. However, improving access to big data related to forest and biodiversity, cloud computing, and digital and satellite technology can help improve adoption of AI technology in India. We hope that this synthesis will motivate forest officials, scientists, and conservationists in India to explore AI technology for biodiversity conservation and forest management.

The significance of renewable energy in achieving necessary reductions in emissions to limit global warming to 1.5 degrees Celsius is widely acknowledged. However, there is growing concern over the allocation of land for constructing the required new infrastructure. Nowhere is this conflict more apparent than in India, where renewable energy targets are ambitious and land use conflicts are already significant. India intends to increase renewable energy to 500 GW by 2030. This would require an additional 42 GW of renewable energy to be installed every year. Although renewable energy can provide the solution to both India’s growing need for cheap energy and climate change mitigation, the sustainable future of renewable energy deployment is far from simple due to its associated land use impacts and socio-ecological risk. While others have highlighted challenges to India’s renewable energy targets, here we focus on the land use change issues that will need to be addressed for India to meet its targets. We introduce a series of recommendations and highlight how these could contribute to mainstreaming land values and facilitate the implementation of India’s 2030 renewable energy targets. These recommendations include suggested planning approaches that would guide the development of standard siting guidelines, identification of preferential “go-to” areas for renewable energy, and the development of tools that allow access to data and information to site renewable right. Policy recommendations highlight utilizing converted lands and existing built infrastructure for renewable energy development, and adapting existing policies so they address land use impacts.

Accelerating solar deployment without intensifying competition for agricultural land is a major challenge for land-constrained energy transitions. Although agrivoltaic (Agri-PV) research has advanced rapidly, spatial assessments and crop-response studies remain largely disconnected, limiting the ability to identify not only where Agri-PV can be deployed but also which agricultural systems are compatible and how much solar capacity they can support. Here, we develop an integrated national-to-district Agri-PV planning framework for India combining Geographic Information Systems (GIS), Multi-Criteria Decision-Making (MCDM), and the Analytic Hierarchy Process (AHP). At 500 m resolution, an Agrivoltaics Suitability Index (ASI) integrates biophysical, technical, agricultural, infrastructural, socio-economic, and environmental criteria. We further synthesize experimental evidence for 154 crop species to incorporate crop-specific shade compatibility and translate spatial suitability into configuration-specific installable solar capacity. We identify 19.59 million hectares (Mha) of agricultural land suitable for Agri-PV, including 12.85 Mha classified as highly suitable. Single- and double/triple-cropped lands account for the largest deployment opportunity, with national installable capacity estimated at 9,278–12,207 GWp under the assessed configurations. Rajasthan, Maharashtra, Andhra Pradesh, Karnataka, Telangana, and Madhya Pradesh emerge as priority states, while district-level analysis reveals substantial spatial heterogeneity within states. The crop synthesis shows that several vegetables, legumes, berries, and medicinal crops can tolerate moderate photovoltaic shading, although responses vary substantially among species. By linking spatial suitability, crop compatibility, agricultural context, infrastructure access, socio-economic feasibility, and energy potential, this study provides an integrated decision-support framework for strategically scaling Agri-PV while limiting agricultural land-use conflicts.