Compensatory mitigation, or biodiversity offsets, provide a mechanism for maintaining or enhancing environmental values in situations where development is being planned, despite detrimental environmental impacts. Offsets are generally intended as an option for addressing any remaining environmental impacts of a development plan, after efforts have been made to avoid, minimize, or restore on‐site impacts. Although offset programs require that developers adhere to the mitigation hierarchy to avoid, minimize, and restore biodiversity on‐site before considering an offset for residual impacts, no quantitative guidelines exist for this decision‐making process. What criteria are needed to require that impacts be minimized or avoided altogether? Here, we examine how conservation planning can provide a way to address this issue. By blending landscape‐level conservation planning with application of the mitigation hierarchy, we can ensure that the use of biodiversity offsets is consistent with sustainable development practices.

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.

Wind energy offers the potential to reduce carbon emissions while increasing energy independence and bolstering economic development. However, wind energy has a larger land footprint per Gigawatt (GW) than most other forms of energy production, making appropriate siting and mitigation particularly important. Species that require large unfragmented habitats and those known to avoid vertical structures are particularly at risk from wind development. Developing energy on disturbed lands rather than placing new developments within large and intact habitats would reduce cumulative impacts to wildlife. The U.S. Department of Energy estimates that it will take 241 GW of terrestrial based wind development on approximately 5 million hectares to reach 20% electricity production for the U.S. by 2030. We estimate there are ∼7,700 GW of potential wind energy available across the U.S., with ∼3,500 GW on disturbed lands. In addition, a disturbance-focused development strategy would avert the development of ∼2.3 million hectares of undisturbed lands while generating the same amount of energy as development based solely on maximizing wind potential. Wind subsidies targeted at favoring low-impact developments and creating avoidance and mitigation requirements that raise the costs for projects impacting sensitive lands could improve public value for both wind energy and biodiversity conservation.

Conservation easements have become the principal tool used by land trusts to preserve habitat and open space. However, anecdotal evidence has led some to question whether easements actually deliver conservation value. Our analysis of data from 119 easements held by The Nature Conservancy (TNC), spanning eight states and 20 years (1984–2004), is the first study to examine temporal patterns in the stated goals of, and activities allowed under, conservation easements. We found that these easements operate in accordance with conservation principles: 96% of sampled easements have identified biological targets, 84% are within TNC priority sites, and 79% are adjacent to protected areas. Easement usage has also become more strategic; recently established easements are more likely than older easements to be large and to include a management plan that focuses on biological targets. The one shortcoming we uncovered is a lack of biological monitoring. Although 92% of sampled easements have been monitored for legal compliance in the past 3 years, only 19.8% of biological targets have been monitored quantitatively. It is clear that we cannot draw conclusions regarding easement effectiveness unless we implement more systematic monitoring.

India has committed to reduce emissions with a goal to increase renewable energy production to 175 gigawatts (GW) by 2022. Achieving this objective will involve rapidly increasing the deployment of solar and wind energy, while at the same time addressing the related challenges of the financing requirements, environment impacts, and power grid integration. Developing energy on lands degraded by human activities rather than placing new infrastructure within natural habitats or areas of high production agriculture would reduce cumulative impacts and minimize land use conflicts. We estimated that converted lands have the potential capacity of 1789 GW across India, which is >10 times the 2022 goals. At the same time, the total land footprint needed to meet India’s 2022 renewable energy target is large, ranging from ~55,000 to 125,000 km², which is roughly the size of Himachal Pradesh or Chhattisgarh, respectively. If renewable energy is advanced with the singular aim of maximizing resource potential, approximately 6700–11,900 km² of forest land and 24,100–55,700 km² of agricultural land could be impacted. Subsidies and incentive programs aimed at promoting low-impact renewable energy deployment and establishing mitigation obligations that raise costs for projects that create land-impacts could improve the public support for renewable energy.

A growing and more affluent human population is expected to increase the demand for resources and to accelerate habitat modification, but by how much and where remains unknown. Here we project and aggregate global spatial patterns of expected urban and agricultural expansion, conventional and unconventional oil and gas, coal, solar, wind, biofuels and mining development. Cumulatively, these threats place at risk 20% of the remaining global natural lands (19.68 million km²) and could result in half of the world’s biomes becoming >50% converted while doubling and tripling the extent of land converted in South America and Africa, respectively. Regionally, substantial shifts in land conversion could occur in Southern and Western South America, Central and Eastern Africa, and the Central Rocky Mountains of North America. With only 5% of the Earth’s at-risk natural lands under strict legal protection, estimating and proactively mitigating multi-sector development risk is critical for curtailing the further substantial loss of nature.