Do data centres cause deforestation, and how does land clearing affect forests?

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1. 1

   Global impacts of transportation infrastructure on forest degradation and loss

   Nature Communications (Zhou et al.)Published Feb 4, 2026Checked Oct 6, 2026

   “Impacts extend up to 5 km with a clear distance decay effect, totaling 4.26 million km2 of forest loss—equivalent to 10.7% of the 2020 global forest extent. The Global South (tropics accounting for 54.8%) faces severe, worsening degradation (2000–2020), while the Global North shows milder impacts, with partial recovery. Critically, 89% of grid cells exhibit conflicting long-term trends across metrics, highlighting the inadequacy of cover-only assessments. We further find that road-linked degradation is tightly coupled with local human activity, and that global protected areas have insufficient capacity to curb ongoing degradation. Differences in impacts among regions suggest that road-linked forest degradation is tied to governance choices—urging integrated transport-forest planning to balance development and conservation. Transportation infrastructure threatens forests, but past studies have been regional or focused on single metrics like forest cover. This global analysis finds road-adjacent forests lose cover, height, and productivity and become more fragmented.”

2. 2

   Mapping global resource driven nature loss in the mining sector from 2001 to 2022.

   Nature communications (Cheng et al.)Published May 28, 2026Checked Oct 6, 2026

   “Here, we introduce a novel approach integrating remote sensing, machine learning, and cloud computing to classify approximately 70,000 mining sites by commodity. Using this newly detailed dataset, we quantify the nature loss associated with 20 extracted commodities, focusing on deforestation and habitat destruction. From 2001 to 2022, mining activities worldwide resulted in the removal of 16,268 km² of forest cover, with 65.64% occurring in tropical and subtropical regions. Notably, approximately half of this deforestation was attributed to the extraction of gold, coal, aluminium (bauxite), nickel-cobalt and copper, primarily in countries intersecting the Amazon, Southeast Asian, and Congo Basin rainforests. Our analysis also reveals that deforestation-to-mining area ratios and biodiversity risks vary by mining location, and conservation threats do not always scale with deforestation rates. By providing commodity-specific maps of mining-induced nature loss, our work equips companies and organisations with actionable insights to identify risks within their supply chains and implement targeted mitigation strategies.”

3. 3

   Mining triggers extensive additional deforestation in sub-Saharan Africa.

   Nature (Morton et al.)Published Jun 3, 2026Checked Oct 6, 2026

   “In total, we find 187,000 hectares of direct mining-driven deforestation, that is, deforestation due to features directly associated with mining operations, such as pits, tailing ponds and spoil heaps. We estimate that mining also triggers an additional 8.0 percentage points (pp; 95% confidence interval (CI): 7.2-8.9 pp) increase in deforestation within 1 km of a mine compared with unmined areas. Increased levels of deforestation (1.1 pp, 95% CI: 0.7-1.5) persist up to 20 km from mines even after ten years. For every hectare of direct deforestation due to the mine footprint, mining triggers, on average, 34 hectares of additional offsite loss within five years through ancillary activities, including agriculture and settlements. Mines extracting cobalt and copper-key energy transition minerals-caused the highest amount of additional deforestation. Embedding offsite deforestation levels into environmental impact assessments for new mining projects will be key to ensuring zero-deforestation or no-net-loss supply chains for critical minerals and reduce future mining-driven forest losses in sub-Saharan Africa.”

4. 4

   Data centre industry in India (Wikipedia)

   WikipediaPublished Oct 7, 2026Checked Oct 11, 2026

   “India has a growing data centre industry. Data centres are used for national security, internet infrastructure, and economic output. As of 2024, India's data centre capacity is at 950 MW, which is expected to be 1800 MW by 2026. The data centre industry is valued at US$1.2 billion in 2021, a 216% growth from $385 million in 2014. The number of data centres in India is 138, as of March 2022. India ranks 13th globally in terms of highest number of data centres. As of 2021, Indian data centres occupy over 8 million sq ft area. 60% of total data centres are in Navi Mumbai, Chennai,Noida, Gurgaon, Bangalore and Hyderabad. India's data centre capacity is projected to experience significant growth, doubling from 0.9 GW in 2023 to approximately 2 GW by 2026. This expansion is driven by the increasing digitisation and data localisation trends within the country. Despite generating 20% of the global data, India currently holds only a 3% share of global data centre capacity, highlighting substantial under-penetration in this sector. The estimated capital expenditure required for this capacity addition is around Rs 50,000 crore over the next three years.”

5. 5

   Flexibility-aware framework for efficient planner-initiated siting of data center.

   Nature communications (Kim et al.)Published May 16, 2026Checked Oct 6, 2026

   “Applied to a synthetic 2000-bus Texas power system, the framework demonstrates that operational flexibility expands the siting frontier by 9-17% at 1 GW and 19-21% at 2 GW compared to firm operation. Median all-hour average prices remain essentially unchanged ($24.32/MWh for the 2 GW cases), and the shift envelope attenuates peak-hour price dispersion by approximately 3.4% with minimal side effects during off-peak hours. Utilizing pre-certified envelopes to bypass major transmission reinforcements, this workflow enables first energization in 12-18 months-a conservative reduction of 3.5-4 years versus the conventional 5-8 year project-led process. This technology-agnostic framework provides a proactive decision-making tool for system operators and regulators to fast-track large flexible loads while preserving grid reliability and market stability.”

6. 6

   Data center (Wikipedia)

   WikipediaPublished Oct 8, 2026Checked Oct 10, 2026

   “A data center is a physical room, building, or facility for storing, managing, and disseminating data and information, including computer systems and associated components, housing IT infrastructure, and training artificial intelligence. The United States' act of Congress, the Energy Independence and Security Act of 2007, defines a data center as "any facility that primarily contains electronic equipment used to process, store, and transmit digital information." This includes "a free-standing structure" or "a facility within a larger structure, that uses environmental control equipment to maintain the proper conditions for the operation of electronic equipment." According to IBM, data centers date back to the 1940s, with the U.S. military's Electrical Numerical Integrator and Computer (ENIAC) as an early example. Data centers have different models depending on their workloads.”

7. 7

   Balancing Bits and Drops: Stress-Adjusted Water Management for Data Centers

   arXiv (Cornell University) (Talukder et al.)Published Jun 15, 2026Checked Oct 13, 2026

   “However, prior research has largely focused on reducing total water use, overlooking that the same unit of water can have drastically different environmental consequences depending on when and where it is consumed. In this paper, we introduce a stress-adjusted water framework that quantifies the true sustainability impact of data center water consumption by incorporating both spatial and temporal water stress. Using the AWARE-US model, we capture county-level monthly variations in water availability and extend this framework to account for the off-site water footprint of electricity generation. Based on this stress-aware accounting, we analyze stress-adjusted water-computing strategies spanning both the software and infrastructure layers. Specifically, we study workload scheduling policies that jointly optimize water and carbon efficiency, evaluate the potential of rainwater harvesting as a supplemental water source, and investigate the feasibility of dry cooling as a water-free alternative to evaporative cooling.”

8. 8

   Governing AI Data Center Growth in Water-Stressed Regions

   Advances in computational intelligence and robotics book series (Khaldy et al.)Published Jun 29, 2026Checked Oct 13, 2026

   “AI data centers are becoming critical infrastructure, but their growth is also a water-governance issue. Cooling systems can raise local freshwater demand directly, while electricity procurement adds indirect water use across power systems. In water-stressed regions, siting decisions affect not only land and grid capacity, but also basin resilience, transparency, and justice. This chapter develops a governance framework for AI data center growth that combines hydrological screening, public disclosure, and water-justice safeguards. It argues that responsible expansion requires place-based regulation that evaluates cumulative withdrawals, drought sensitivity, emergency curtailment, community participation, and enforceable reporting. Drawing on research on data center sustainability, water disclosure, planning, and environmental justice, the chapter proposes policy instruments for governments, utilities, and operators seeking water-resilient digital infrastructure.”

9. 9

   Comprehensive security review and challenges for smart and power grids to prevent cyber-attacks in IoTs.

   MethodsX (Kabier et al.)Published Jul 13, 2026Checked Oct 11, 2026

   “This SG offers resilient, adaptive, self-recovery, and sustainable power distribution. The recent past has seen a gradual shift from unidirectional power systems towards the SG's bi-directional model. However, these large, complex, and self-healing SG systems have numerous vulnerabilities that threaten the exchanged data and power control commands. Therefore, many research efforts have been directed towards the enhancement of the SG security. In this paper, an overview of threats, attacks, vulnerabilities, security measures, and limitations for the IoT-based SG are described. We also give detailed descriptions of the Supervisory Control and Data Acquisition (SCADA) architecture as well as cloud computing as critical components of the smart grids. Moreover, this survey paper discusses tools and methods for verifying security protocols. The outcomes indicate the necessity of ensuring Quality of Service (QoS) in various communication technologies and networks deployed in SGs. The findings also point to the significance of developing network models and middleware to enhance QoS of the smart grid networks, which collect various data from diverse smart components and sensors.”

10. 10

    Cyber-resilient Intensive Care Unit: Ransomware is a Patient-safety Crisis-A Resource-stratified Approach for India.

    Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care MediciPublished May 25, 2026Checked Oct 11, 2026

    “India faces a compounded risk-the 2022 All India Institute of Medical Sciences (AIIMS) Delhi ransomware attack exposed fragmented systems, an absence of post-incident clinical surveillance, and the operational reality that "Western" cybersecurity fixes do not translate directly to resource-variable settings. This Viewpoint reframes cyber-resilience as a bedside safety competency rather than an information technology (IT) concern and proposes a resource-stratified three-tier preparedness framework. Tier 1 defines minimum viable controls achievable by any ICU: Downtime kits, paper medication administration records (MARs), designated roles, and structured drills. Tier 2 adds targeted redundancy; tier 3 outlines advanced capabilities for tertiary centers. When digital systems fail, patient survival depends on analog competencies practiced before the crisis.How to cite this articleChoudhuri B, Prakash J, Pal B, Veenith T. Cyber-resilient Intensive Care Unit: Ransomware is a Patient-safety Crisis-A Resource-stratified Approach for India. Indian J Crit Care Med 2026;30(5):363-367.”

11. 11

    Energy, Exergy, Economic, Environment Analysis and Multiobjective Optimization of the Organic Rankine Cycle Integrating an Absorption Heat Transformer Driven by Geothermal Energy for Data Center Power Supply and Waste Heat Recovery.

    ACS omega (Chen et al.)Published Aug 25, 2026Checked Oct 11, 2026

    “Exergy analysis and comprehensive performance assessment are conducted to identify the irreversibility and quantitatively assess the advantage of the proposed system in economic and environmental aspects. The results indicate that the proposed system can achieve a net power output of 1690.44 kW and an energy recovery efficiency of 56.90% under optimal conditions, with an annual cost of 886.67 kUSD and lifecycle GHG emissions of 1083.31 ton CO2‑eq. Compared with conventional systems without waste heat recovery, the proposed configuration reduces the annual cost and GHG emissions by 11.11% and 37.69%, respectively, while achieving significant improvements over grid-powered scenarios. Exergy analysis reveals that, to balance thermodynamic, economic, and environmental performance, the system sacrifices part of its thermodynamic efficiency, which leads to increased thermal mismatches during heat transfer, and the primary irreversibility is contributed by the ORC evaporator and condenser, accounting for over 65% of total exergy destruction.”

12. 12

    Global data-water symbiosis reduces AI infrastructure's carbon and water footprint.

    Environmental science and ecotechnology (Wang et al.)Published Apr 26, 2026Checked Oct 10, 2026

    “The analysis reveals a strong global spatial co-occurrence between data centres and WWTPs, enabling optimized national-scale pairings in which treated effluent is used for data centre cooling and the return heat is recovered to support sludge drying and anaerobic digestion. This symbiotic approach reduces greenhouse gas emissions by approximately 84 million tonnes of CO2 equivalent annually, conserves approximately 1300 million m3 of freshwater, and provides net annual cost savings of approximately US$95.4 billion. The greatest mitigation and water-saving potential lies in the United States, Japan, China, the Netherlands, and the United Kingdom. These findings establish data-water symbiosis as a readily scalable infrastructure solution that decouples AI from its carbon and water footprints. WWTPs are poised to evolve from disposal facilities into critical energy-coupling hubs, enabling efficient thermal and water exchange across urban systems and accelerating progress towards multiple Sustainable Development Goals.”