Climate co-benefits of tiger conservation

The study addresses how biodiversity conservation policies can provide measurable climate change mitigation co-benefits. While biodiversity conservation and climate mitigation have often been treated separately, nature-based solutions that protect and restore habitats can serve both goals. Biodiversity-first interventions may preserve natural carbon stocks and unlock new conservation funding pathways, such as carbon markets, but questions about additionality and effectiveness remain. India’s national tiger conservation strategy—Project Tiger and the establishment of the National Tiger Conservation Authority (NTCA)—designated tiger reserves with enhanced management, protection, monitoring, and funding. The authors hypothesized that this enhanced protection would reduce deforestation in treated reserves by mitigating local deforestation drivers (for example, extraction, encroachment) and thus yield quantifiable avoided emissions and economic benefits.

Prior work highlights strong linkages between biodiversity conservation and climate mitigation, with nature-based climate solutions offering synergistic benefits. Projects that explicitly integrate biodiversity co-benefits often see greater market preference than carbon-only projects. Conversely, protected areas can be under-resourced ‘paper parks’ that fail to prevent degradation, indicating a funding gap and the need for effective management. Carbon markets and payments for ecosystem services are expanding, but additionality requirements typically exclude already protected areas. Studies on deforestation drivers point to roles of accessibility, roads, poverty, and climatic and biophysical factors. This literature motivates a biodiversity-first evaluation of climate co-benefits and informs the selection of covariates and causal inference methods used here.

The authors quantified the forest carbon storage co-benefits of India’s tiger reserve policy using a synthetic control approach. Treatment units were protected areas designated as tiger reserves (n=45) with intervention years between 2007 and 2015; donor units were protected areas with tiger presence not designated as tiger reserves (n=117). The response variable was annual cumulative tree cover loss (2001–2020) per reserve derived from Global Forest Change data on Google Earth Engine. Covariates for matching captured structural drivers of forest loss: human population density (WorldPop), precipitation (ERA5), elevation, slope, aspect (SRTM), aboveground biomass in 2000 (Global Forest Watch), road length within reserves (gROADS), local purchasing power parity (G-Econ), age of protected area, and minimum travel time to a city (>50,000 population). Reserves were grouped into landscape complexes, with donor pools selected within merged regional groupings to ensure ≥20 donor units per model. Synthetic control models were implemented in R (tidysynth) using the LowRankQP optimizer. Goodness of fit was visually assessed and significance tested via placebo units and two-sided Fisher’s exact tests comparing pre- vs post-intervention mean squared prediction error (MSPE) ratios; only models with unadjusted P<0.05 were retained. Robustness checks included: (1) anticipation effect tests using a pseudo-intervention in 2005 (reserves showing significant effects under this test were excluded); (2) area-based donor pool trimming (donors ≥10% and ≥25% of treated reserve area) to assess direction, magnitude (within ±20%), and significance robustness; and (3) bootstrap hypothesis tests (9,999 iterations) comparing pre-intervention MSPE across groups. Avoided forest loss per reserve was the 2020 difference between the synthetic counterfactual and observed cumulative loss. Avoided emissions were calculated using reserve-specific above- and belowground biomass carbon densities (2010 baseline scaled to intervention year), a 10-year decay for belowground pools, and converted to CO₂e using the IPCC factor 3.67. Economic valuation included (a) avoided social cost of carbon at US$86 per tCO₂e for India and (b) potential carbon offset revenue at US$5.8 per tCO₂e (voluntary market average).

• Across 162 protected areas with tiger presence (2001–2020), total forest loss was 61,648 ha (3,082 ha yr−1). Untreated reserves (n=117) accounted for 47,719 ha (77%; mean 408 [95% CI: 190–626] ha per reserve). Treated tiger reserves (n=45) lost 13,289 ha (23%; mean 309 [127–496] ha per reserve). The highest observed deforestation occurred in Kotgarh Wildlife Sanctuary (Odisha): 8,927 ha (28% of 2000 forest). Bor Wildlife Sanctuary (treatment) had zero loss.
• Of 45 treated reserves, 15 showed significant effects (after excluding anticipation effects). Of these, 11 had significant avoided deforestation totaling 6,558 ha since 2007; 4 had significantly higher-than-expected forest loss totaling 756 ha. Net avoided forest loss across significant reserves was 5,802 ha.
• Largest avoided forest loss: Nawegaon-Nagzira (Maharashtra), 2,645 ha since 2013. Largest additional loss relative to counterfactual: Pilibhit (Uttar Pradesh), >300 ha since 2008. No reserve in the Northeast Hills and Brahmaputra region showed avoided deforestation.
• Model quality: No significant difference in pre-intervention MSPE between significant (n=15) and non-significant (n=30) models (P=0.21) or between northeastern vs other regions (P=0.49). Covariate distributions were comparable between donor and treatment groups; results were robust to area-based donor trimming and placebo tests.
• Avoided emissions and valuation (significant reserves): Net avoided emissions were 1.08 ± 0.51 MtCO₂e, comprising 0.85 ± 0.33 (aboveground) and 0.23 ± 0.18 (belowground) MtCO₂e. This corresponded to US$92.55 ± 43.56 million in avoided social costs and US$6.24 ± 2.94 million in potential carbon offset revenue. For the 11 reserves with avoided deforestation only, avoided emissions were 1.28 ± 0.59 MtCO₂e, valued at US$110.29 ± 50.87 million (social cost) and US$7.44 ± 3.43 million (offsets). The 4 underperforming reserves added 0.21 ± 0.09 MtCO₂e, implying US$17.74 ± 7.32 million in damages and US$1.2 ± 0.49 million in lost potential offset revenue.
• Table 1 reports per-reserve avoided loss and valuation for the 11 reserves with avoided deforestation; top contributors by ecosystem service value included Nawegaon-Nagzira, Similipal-Hadagarh, and Udanti-Sitanandi.

The enhanced protection associated with tiger reserve designation yielded a net positive impact on forest protection and climate mitigation, with most significant cases showing avoided deforestation. Positive outcomes were pronounced in Central India and in reserves with higher initial forest cover, improving landscape connectivity (for example, Nawegaon-Nagzira). Likely mechanisms include improved fund management via local conservation foundations, community benefit sharing (for example, ecotourism), and adoption of GPS-enabled monitoring technologies enhancing patrol effectiveness. Underperformance in some reserves—especially in Northeast India—may be driven by region-specific pressures (encroachment, shifting agriculture, illegal timber trade, mining) and enforcement challenges linked to remoteness and lower development. Anamalai’s losses may relate to plantation-driven fragmentation; Pilibhit’s recent designation and protected-area age effects may explain outcomes despite growing tiger populations. The study underscores potential links between human–wildlife conflict and deforestation that warrant investigation. Economically, the intervention’s avoided emissions translate into substantial ecosystem service value (~US$93 million net), suggesting that biodiversity investments can yield measurable climate and economic returns, and that broader application could further enhance benefits (for example, an estimated additional US$38 million if extended to untreated reserves). While carbon markets could mobilize funding, current additionality rules often preclude enhanced management in already protected areas; revisiting criteria to recognize management-driven additionality could unlock financing, provided equity and community considerations are central.

Designating tiger reserves in India delivered measurable climate co-benefits through avoided deforestation and associated emissions reductions, demonstrating the feasibility of quantifying climate mitigation additionality from species-focused conservation. The synthetic control framework provides a robust, scalable approach to attribute avoided forest loss and value ecosystem services. Integrating conservation policies with carbon financing mechanisms—while addressing additionality and ensuring equitable community benefits—could help close conservation funding gaps. Future research should (1) develop comprehensive datasets on forest degradation to capture additional carbon dynamics beyond outright loss, (2) compile systematic data on human–wildlife conflict to examine links with conservation performance, and (3) test the transferability of this biodiversity-first framework across other high-carbon ecosystems and species conservation programs.

• Carbon co-benefits primarily apply to species inhabiting high-carbon ecosystems; generalization to low-carbon habitats is limited.
• Protected area boundary data were sourced from OpenStreetMap (user-generated), introducing potential spatial uncertainties; alternative databases have incomplete coverage for India.
• The study estimates avoided emissions from deforestation only; exclusion of forest degradation likely leads to conservative estimates.
• Additionality constraints in current carbon market methodologies may limit monetization of the quantified benefits from enhanced management in already protected areas.
• Regional factors (for example, remoteness, enforcement capacity) and unobserved local drivers may influence outcomes and are not fully captured despite extensive covariates.