Conference Agenda

Biodiversity finance is central to achieving the Kunming-Montreal Global Biodiversity Framework (GBF). Yet, donor reporting remains fragmented and inconsistent. We introduce a three-dimensional framework that distinguishes the goal, instrument, and ecosystem of biodiversity development finance. Using large language models (LLMs) to apply this framework, we classify 5.4 million official development finance (ODF) projects from 2000–2023. Despite an increase from USD 2.8 to 15.1 billion over this period, we estimate that biodiversity finance will remain 16-43% below the GBF Target of USD 30 billion annually by 2030. Biodiversity finance is concentrated in activities (e.g., 31% in pollution-control infrastructure) and spatially (e.g., 24% in China, India, Colombia, Brazil, and Vietnam). Overall, we find that the allocation of funding aligns well with countries’ ecosystem needs, as measured by species richness and ecological restoration potential. The presented framework can help monitor and evaluate biodiversity finance across sources globally.

Despite the growing attention to biodiversity losses across the world, there

are considerable concerns of whether this leads to actual behavior for protection. Using a sample of Chinese listed firms from 2008 to 2023, this paper

firstly demonstrates a clear biodiversity attention–behavior gap. This gap has

been narrowing in recent years, which is arguably due to the changing external environment. Using the adoption of the Kunming Declaration in 2021

as a quasi-natural experiment, we show that the biodiversity-sensitive firms

exhibit stronger responses and engage more actively in environmental protection investment. Further analyses explore the role of external governance,

at the government, public, and investor levels, in the attention-behavior relationship. Results confirm that the enhancement of external governance

against biodiversity challenges can help bridging the gap. Overall, our results

highlight the importance of government enforcements and public awareness in

driving corporate eco-friendly actions and avoiding potential greenwashing.

Biodiversity underpins ecosystem services essential to human well-being, yet it is declining at unprecedented rates worldwide. Agri-environmental policies are a central tool for halting these losses, but systematic evidence on which measures effectively conserve biodiversity remains scarce, particularly at the global scale. This paper addresses this evaluation gap by providing a large-scale, cross-country assessment of the effectiveness of agri-environmental policy. We develop a conceptual framework linking policy instruments and targets to biodiversity outcomes and apply it to classify over 200,000 policy documents using large language models. This results in a novel, comprehensive, global database of over 14,000 agri-environmental policy measures implemented between 1988 and 2022. The policy data reveal how policymakers design biodiversity-related interventions and which conservation targets they prioritize. To identify effective policies, we use a machine learning-based extension of difference-in-differences approach and measure biodiversity outcomes using the Biodiversity Intactness Index for more than 170 countries. Our preliminary results identify 38 cases of significant biodiversity improvements across diverse biomes, associated with policy mixes aiming to improve species richness and ecosystem integrity and protect genetic diversity. These findings provide actionable insights for policymakers seeking to design agri-environmental policies that deliver measurable biodiversity gains.

As China is a major contributor to global greenhouse gas emissions, its 2060 carbon-neutrality commitment is pivotal to international climate mitigation. Achieving this goal requires credible evidence on which provincial climate policy measures deliver scalable emissions reductions, evidence that is often obscured by overlapping regulations and heterogeneous enforcement. We conduct a systematic ex-post evaluation of roughly 2,400 provincial low-carbon policy measures implemented between 2007 and 2022. Moving beyond traditional econometric designs, we develop a machine learning-based reverse causal inference framework that first detects structural breaks in sectoral carbon dioxide emissions — power, industry, buildings, and transport — and then attributes these shifts to specific local policy measures.

We identify 53 policy interventions that successfully reduced emissions, collectively delivering a 6.5 billion metric tonnes of carbon dioxide abatement, equivalent to approximately 4.3\% of total scope 1 emissions across all regions and sectors between 2007 and 2022. The distribution of realized mitigation is highly uneven: industry contributes the largest share of observed reductions, while buildings and transport display stronger average responsiveness once policy packages become sectorally aligned.

The results reveal a clear disconnect between policy volume and mitigation effectiveness. Policy-rich provinces such as Shanghai do not necessarily exhibit detectable structural breaks, whereas repeated breaks in provinces such as Yunnan, Henan, Inner Mongolia, and Shandong emerge under more selective policy configurations. Across sectors, the industrial sector is governed almost exclusively through integrated policy mixes, whereas single market-based instruments are frequently sufficient in the power and transport sectors. We further find that policy stringency does not systematically rise at the onset of structural breaks; instead, breaks are associated with concentrated pressure within specific instrument–objective combinations rather than with broad-based policy escalation. These findings support a shift toward targeted, portfolio-focused policy design to better translate climate ambitions into realized mitigation outcomes.

Biomass carbon losses from land use and land cover change have increased in recent decades, driven largely by agricultural land expansion. In response, governments worldwide have adopted a wide range of land conservation policies. Here, we assess how these public policies affect land conservation patterns and associated biomass carbon changes, at the global scale, across different policy schemes and climate zones. We further identify specific policy interventions that stand out as particularly effective. Using global geospatial datasets and a combination of econometric and machine learning methods, we estimate that land conservation policies have globally reduced losses of intact and non-intact forest by 30% and by 36%, respectively. In contrast, their effect on grassland conservation is less clear. The observed forest protection has been achieved through both environmental payment schemes and habitat conservation regulations. We further find that large-scale frameworks with financial and technical support from international organizations are critical for mitigating land-use emissions in the tropical regions, whereas integrated national policies that combine regulatory measures with payments are most effective in the temperate regions.

Using data for more than 2,500 counties in China in 2000, 2010, and 2020, we estimate the impacts of changes in cooking solid-fuel use on population health. We find that a 10-percentage-point reduction in the county-level prevalence of solid fuels for cooking is associated with a decrease of approximately 7.1 deaths per 100,000 in the age-standardized mortality rate and an increase of 0.13 years in life expectancy. The health gains are most pronounced early in the life course, and long-run benefits are substantially larger for women than for men. Interaction analyses further indicate that higher educational attainment and the presence of separate kitchens attenuate the adverse health effects of solid-fuel use, with the magnitude of attenuation varying by gender and age group. Building on these estimates, we translate the health effects into inter-period counterfactual annual death and life year differentials and monetize the associated health and economic returns. We find that the observed decline in solid-fuel cooking implies approximately 435,000 avoided deaths and 570 million life-years gained in 2020 (relative to a counterfactual holding each county’s solid-fuel prevalence at its 2010 level). We then construct county-level health–economic return maps using standardized metrics. The maps reveal pronounced spatial heterogeneity and strong geographic concentration in the benefits of the household energy transition: in the highest-benefit counties, avoided deaths in 2020 amount to up to ~12% of observed deaths, and monetized benefits reach up to ~2% of local GDP. Importantly, hotspots defined by avoided deaths are not identical to hotspots defined by monetized benefits, reflecting income-scaled valuation and highlighting distributional trade-offs for policy prioritization. Taken together, these results provide county-level evidence on the population-health benefits and distributional implications of the household energy transition and offer policy relevant guidance for targeting clean cooking promotion alongside complementary investments in human capital and housing conditions.

In the past decades, illegal gold mining has sharply increased in the Tropics. Despite its importance in sustaining livelihoods, it creates negative externalities like increases in malaria due to increasing the habitat suitable for mosquitos. However, the impact of illegal gold mining on malaria remains largely unexplored. Using a 6-year panel dataset at the municipality level and heterogeneity-robust difference-in-differences estimators, we provide evidence of the impact of illegal gold mining on malaria incidence in the Brazilian Amazon, an understudied location with increasing illegal gold mining and high burden of malaria. We find that a 10% increase in illegal gold mining leads to, on average, an increase of 515 malaria cases per 100,000 inhabitants annually. This increase does not seem to be explained by increases in population density or decreases in health expenditures, or deforestation. This increase in malaria transmission translates into social costs ranging from US$39,434 to US$1,725,971 annually due to increases in mortality, cost of treatment, and foregone wages. Our analysis highlights the social cost of unregulated gold mining and the need for the adoption and enforcement of monitoring and mitigation practices concomitant of mining.