Forest conservation remains a major challenge in many developing countries, where institutional barriers contribute to deforestation and persistent poverty. This paper examines a forest tenure reform that devolved forest rights from village collectives to individual households and its impact on forest outcomes. Using two waves of nationwide village and household surveys, satellite imagery on land cover and Enhanced Vegetation Index (EVI), and village-level meteorological records over a 10-year period, we evaluate the reform's effects on forest coverage and quality. The results show that transferring tenure rights to households significantly increased forest coverage in villages. However, improvements in forest quality-measured by vegetation density (EVI)-emerge more slowly. The initial expansion of forest area through newly planted trees temporarily lowers average vegetation density, but canopy density increases as trees mature over time.

Rainfall erosivity (R-factor) is a critical parameter for soil loss prediction and ecological risks assessment, yet its quantification is often limited by the coarse temporal resolution of available precipitation data. While high-frequency observations provide a more accurate characterization of rainfall kinetic energy, a national-scale assessment of the uncertainties induced by these varying data resolutions in China has been lacking. This study presents a new, high-precision annual rainfall erosivity dataset for China (2015–2023) derived from hourly precipitation records across 57,740 meteorological stations (at least 5 years). Using the hourly-derived erosivity (Rhourly) as the benchmark, we systematically evaluated the estimation biases of widely used daily and monthly empirical models. Results show that Rhourly exhibits a decreasing gradient from southeastern to northwestern China, peaking at over 8,000 MJ mm ha−1 h−1 y−1 in the Southern Red Soil Hilly Region. Nationwide, daily-scale estimates show a mean absolute error of 20.7%, transitioning from systematic underestimation in the southeast to severe overestimation in the northwest. Monthly-scale estimations yield a higher mean absolute error of 25.8%, with intensified underestimation in southern regions and a reversal of error direction in the northwest. Across China, the interannual coefficient of variation averages 59.0%, with the highest variability (93.8%) observed in the Northern Wind-Sand Region. By providing station-based benchmark data and identifying spatial error patterns, this dataset offers a robust foundation for high-precision soil erosion mapping, regional conservation planning, and climate change impact assessments.

China’s key forested region is located in the northeast and consists of state forest enterprises which manage forest harvesting and reforestation. Deforestation is a major problem there and has resulted in several central government reforms. We develop a framework for assessing the social cost of state forest enterprise deforestation. We first develop a two-principal, one-agent model that fits the federalistic organization of state forests, in that state forest managers make (potentially hidden) decisions under influence of provincial and central government policies. This model is used to derive an expression of the social cost of these hidden actions. We then use panel data from a survey conducted by Peking University to compute social welfare losses and to formally identify the main factors in these costs. A sensitivity analysis shows that, interestingly, command and control through lower harvesting limits and a more accurate monitoring system are more important to lowering social welfare losses than conventional incentives targeting wages of forest managers. Through regression analysis we also find that the more remote areas with a higher percentage of mature natural forests are the ones that will always have the highest social welfare losses.

This paper investigates the impact of China's second round of forest tenure reforms on rural household labor allocation and migration. Using panel data from 1,210 households across five provinces, we analyze household decisions regarding labor allocation to forestry, agriculture, and off-farm employment. Results show strong evidence that secure forest tenure increases household labor devoted to forestry and modestly encourages return migration, particularly during the 2008-09 financial crisis. Evidence for the safety net function of forests is mixed, with benefits concentrated among better educated households. These findings highlight both the opportunities and limitations of forest tenure reform in addressing rural underemployment, moderating migration pressures, and providing welfare support during economic stress. The study contributes to broader debates on tenure reform, rural development, and the socioeconomic role of forests in developing countries.

China's collective forest tenure reform can play a crucial role in advancing low-carbon transformation, enhancing rural livelihoods, and supporting sustainable forest management. By decentralizing governance, the reform empowers local communities to manage forests sustainably, though significant challenges persist. This study evaluates the reform's effectiveness a decade after its formal conclusion in 2010, based on a comprehensive survey conducted by the National School of Development at Peking University across ten representative collective forest regions. Using a multi-stakeholder approach, the study incorporates perspectives from rural households, forestry enterprises, cooperatives, and local government officials. Findings reveal persistent issues, including limited financial incentives, policy inconsistencies, and barriers to scaling sustainable forestry practices. Addressing these challenges is essential to bridge the gap between policy objectives and on-the-ground realities. This paper provides evidence-based recommendations to optimize policy frameworks, improve implementation strategies, and ensure that collective forest tenure reform contributes effectively to China's sustainability and development goals.

This paper investigates whether China's collective forest tenure reform reduces rural consumption inequality. Using panel data from three survey waves across seven southern and southeastern provinces, we measure intra-village inequality as the absolute deviation of each household's consumption from the village average and apply inverse probability weighting to correct for sample attrition. Results show that fully transferring forest management rights from collective to household control reduces inequality in daily consumption, housing consumption, and total consumption by 16.0%, 24.3%, and 14.8%, respectively. The reform disproportionately benefits previously disadvantaged households by im proving access to forest resources, expanding income-generating opportunities, and reducing reliance on collective decision-making, thereby narrowing intra-village disparities. These findings provide new evidence that decentralizing land tenure can enhance rural equity and offer policy implications for forest-rich developing countries seeking to promote both social stability and inclusive rural development through tenure reforms.

This paper uses a collective decision model framework to analyze the outcomes of China's forest tenure reform. The reform essentially allows redistribution of residual clamant rights over collective owned forests, through a bargaining process between two groups, the village elites and the ordinary farmers. We specify a set of distribution factors which determine bargaining powers for the respective groups, which in turn determine the final outcomes of the reform. Using data collected through two rounds of nationwide surveys, we identify that community level social capital, policy transparency, and farmers' outside options, along with other socio-economic conditions, are critical to determine ordinary villagers' share of forestland, while the long-term established authority and managerial ability of the village leaders, etc. are strong determining factors for the share of forestland remained under collective leadership control. Policy implications point to the need for regular and high-quality village election, if policy makers would like to see greater decentralization of forest tenure in rural society.

Extreme temperatures threaten agriculture and exacerbate global food insecurity, yet their direct impact on dietary choices remains poorly understood. We provide the first evidence of how short-term exposures to hot or cold weather may affect macronutrient intake in China. We find that hot weather reduces carbohydrate and protein consumption but not fat intake, while cold weather increases all nutrient intakes, particularly fats. Both conditions elevate high-fat diet risks. Fans, air conditioners, and heating systems mainly mitigate these effects by altering thermal comfort, whereas refrigerators, which primarily serve to store food, show minimal impact. These results suggest that temperatures may influence dietary patterns more through physiological appetite regulation than food accessibility. Socioeconomic disparities are evident, with rural and less-educated individuals more likely to adopt high-fat diets. Projections indicate that climate change will generally increase high-fat diet probabilities, with northern regions experiencing declines and southern regions rising due to differing temperature changes. Significance statement:Climate change poses an increasing threat to global food security, and earlier research primarily focused on how temperature affects agricultural supply. Since the specific effects on individual dietary behavior remain poorly understood, this study presents the first systematic evidence linking extreme temperatures to changes in macronutrient intake. It demonstrates how both heat and cold can influence household food consumption. These shifts in diet, particularly towards higher-fat foods, increase the risk of obesity. It may lead to significant public health challenges, especially in countries where healthcare costs associated with obesity are projected to rise sharply. These insights are essential for developing integrated strategies to mitigate the impacts of climate change on human health and long-term food systems.

Global warming intensifies hydroclimatic variability, driving complex dynamics of snow cover area (SCA). The Tianshan Mountains (TS), a critical water reservoir for Central Asia, are highly sensitive to climate fluctuations with broad hydrological and societal implications. This study examined SCA-climate interactions (1990-2020) and developed a novel ensemble model to project future changes. Results revealed a net SCA expansion of 16.18 x 103 km2 (0.58 x 103 km2 yr-1), averaging 14.68 x 104 km2 annually, concurrent with warming (0.323 degrees C decade-1) and drying (-16.138 mm decade-1). Precipitation and SCA exhibited pronounced spatial coherence, both peaking in the northwestern mountains and declining toward the southeastern plains. Climate-driven thermal forcing and precipitation phase reshaped snow regimes, with permanent snow retreating to seasonal cover and modest winter increases. Subregional climatic controls on SCA diverged: precipitation dominated the River Valley Zone (43.01 %), whereas temperature governed the North (37.40 %) and South Slope (19.59 %) Zones. The ensemble model combining CA-Markov, FLUS, and PLUS enhanced prediction accuracy through synergistic calibration and automatic weight allocation (AWA). Projections indicated further SCA expansion by 2050, primarily seasonal cover advancing over bare soil. This expansion was episodic and condition-dependent, reflecting the precipitation-compensation effect that temporarily yields snow gains but remained constrained by westerly moisture transport and regional warming. Future warming beyond critical thresholds may nullify this effect, leading to a decline in SCA.

This study explores how the application of democratic rule in land reform decision-making determines villagers' political trust towards different levels of the government in China. Analyzing a two-period household survey dataset, we find that in China's recent Collective Forest Tenure Reform, which has devolved the tenure rights of the village collective-owned forestland to households, democratic decision-making increases trust for town and county cadres. The impact on trust towards village cadres is significant only when democracy involves all villagers in a village. We show two mechanisms that improve villagers' trust: the "privatization" effect, where democratic decision-making leads to more land devolved to villagers, and the "conflict-resolving" effect, where improved information and cohesion by mass participation helps resolve inter-village land disputes. Heterogeneity analyses show that democratic decision-making has a more pronounced effect in improving trust for villagers with lower income, and those without affiliation with the Chinese Communist Party or village committees.

In 2000 China launched the Natural Forest Protection Program (NFPP) as its flagship initiative for forest conservation and restoration, targeting both state-owned forestland managed by state-owned forest enterprises (SOFEs) and large areas of forestland held by village households. This study evaluates the overall impact of the NFPP on forest cover and examines the program's heterogeneous effects across property right regimes and provinces using a spatial regression discontinuity design. Our analysis reveals that forest cover within NFPP boundaries is, on average, about 6 % higher than in adjacent areas. Notably, collective forestland experiences an 82 % greater treatment effect compared to state-owned forests - even though collective areas receive less direct financial support - underscoring the role of institutional and local governance factors. Furthermore, our findings highlight significant regional variations in program outcomes. Overall, the NFPP exemplifies a proactive approach to reversing deforestation amidst rapid economic development, and our results offer valuable insights for refining policy measures and ensuring equitable funding strategies across diverse forest management regimes.

Current global multisource merged precipitation datasets can facilitate better utilization of the complementary nature of gauge-, satellite-, and reanalysis-based precipitation estimates, particularly for capturing precipitation variability. However, merging these datasets at high resolutions of 1-hourly and 0.1 degrees on a full global scale remains a substantial challenge for the scientific community owing to high spatiotemporal heterogeneities. This study proposes a merging-and-calibration framework to optimally integrate the advantages of gauge-, satellite-, and model-based precipitation estimates, focusing on precipitation occurrences and providing a new fully global multisource merging-and-calibration precipitation (GMCP: 1-hourly, 0.1 degrees, global, 2000-the present) dataset. The main conclusions included 1) GMCP generally outperformed the input datasets, ERA5-Land, GSMaP-moving vector with Kalman filter (MVK), and IMERG-Late, across various spatiotemporal scales, both in regional statistics and extreme precipitation systems; 2) GMCP significantly outperformed IMERG-Final, calibrated by gauge analysis at the monthly scale, with the improvements in correlation coefficient (CC), root-mean-square error (RMSE), and Heidke skill score (HSS) by approximately 66.67%, 39.25%, and 26.83%, respectively, from 2016 to 2020 over the contiguous United States (CONUS); 3) compared to the state-of-the-art multi-source merged product with a daily gauge correction scheme, Multisource Weighted-Ensemble Precipitation (MSWEP) V2 (3-hourly and 0.1 degrees), GMCP demonstrated the notable improvements with an approximately 20% enhancement in accurately capturing the precipitation occurrences against approximately 67 000 rain gauges over mainland China in 2016; 4) in comparison to another well-known multisource merged quasi-global daily and 0.05 degrees precipitation product, Climate Hazards Infrared Precipitation with Stations (CHIRPS) integrating the gauge-, satellite-, and reanalysis-based precipitation estimates, GMCP also demonstrated the notable improvements at the daily scale, achieving the increases in CC, RMSE, and HSS by around 57.45%, 38.18%, and 75.76%, respectively, against approximately 67 000 rain gauges over mainland China in 2016; and 5) this framework was suitable for generating the fully global precipitation datasets at 1-hourly and 0.1 degrees scales, significantly mitigating the inherent shortcomings of each input dataset, with GMCP demonstrating the great potential as a valuable resource for worldwide scientific research and societal applications. SIGNIFICANCE STATEMENT: Highly accurate global gridded precipitation datasets for precipitation occurrences and volumes are essential for understanding the water, energy, and carbon cycles on Earth in the context of a changing climate. This study aimed to introduce a new fully global multisource merged precipitation dataset with high quality and resolutions of 1-hourly and 0.1 degrees from 2000 to the present. This dataset integrated the advantages of ground gauge-, satellite-, and model-based precipitation estimates, particularly regarding precipitation occurrence, which can benefit scientific research communities and societal applications worldwide, including hydrological, climatological, meteorological, and water resource management.

China’s economic growth has come at the expense of environmental quality and the degradation of natural resources. In this paper, we identify two sources of environmental degradation: career concerns by managers of state-owned forest enterprises (SFEs) that manage natural resources, and asymmetric information between managers and their superiors regarding the SFEs’ environmental performance. A manager of such an SFE is the agent with two principals: national and sub-national governments. As well as needing to meet ecological targets imposed by the national government, a manager wants profits and promotion into the ranks of sub-national government. We develop hypotheses based on a theoretical model and test them on China’s northeastern SFEs by combining satellite imagery on deforestation with economic survey data. We find that deforestation is more likely for managers of SFEs that have a larger area and are thus more difficult to monitor with respect to ecological targets. Furthermore, we find that sharing a larger proportion of profits with the sub-national government increases the likelihood of getting promoted.

Multiple spectral infrared (IR) observations onboard geostationary satellites are effective and widely used in estimating precipitation with high spatiotemporal resolutions. Currently, FengYun-4A (FY-4A) and FengYun-4B (FY-4B), representing the most advanced Chinese geostationary meteorological satellites, are equipped with Advanced Geosynchronous Radiation Imager (AGRI) to continuously observe the climate and weather over vast eastern Asia region. However, geographic factors, such as viewing zenith angle (VZA) and solar zenith angle (SZA), could result in systematic errors in estimating and merging precipitation. Therefore, motivated by analyzing the influence patterns of these physical factors on precipitation estimation, the precipitation estimation using the chromatographic analysis method by merging enhanced multispectral IR observations (PECAM) is proposed to generate the merged precipitation data covering observed fields of both FY-4A and FY-4B. The main conclusions are summarized as follows: 1) latitude, view zenith angle (VZA), and elevation exert a negative influence (up to 20 K) on averaged TBBs of single IR band (as T-10.8), causing precipitation overestimation; 2) Delta T7.1A-13.5A and Delta T6.95B-13.3B, Delta T3.75H-13.5A and Delta T3.75H-13.3B , and Delta T3.75H-3.75L are mainly influenced by ecliptic obliquity angle (EOA), SZA, and shadow effect, causing seasonal patterns, diurnal fluctuations, and shadow effects, respectively; 3) compared to PERSIANN-CCS, FY4A-Official, and FY4B-Official, at hourly scale, PECAM-FY4A&B consistently outperforms them across CC, RMSE, and CSI metrics, with minimum improvements of approximately 0.046 in CC, 0.30 mm/h in RMSE, and 0.033 in CSI; and 4) meanwhile at daily scales, merged data from FY-4A&B shows overall improvements in CC, RMSE, and CSI, with at least 0.041, 0.80 mm/day, and 0.022, respectively. Foreseeably, the signal processing and merging strategy in PECAM has significant potential to serve as references for the estimation and integration of precipitation data from the FY-4 series, as well as the GOES, Meteosat, and Himawari series.

China is the world's largest aquaculture producer with a diverse sector in terms of species, technologies, and production environments. Production volume has grown substantially since the late 1970s, reaching 52.2 million metric tonnes in 2021. However, the Chinese aquaculture sector has also faced sustainability challenges. In this paper, we use the Aquaculture Performance Indicators to assess twelve important aquaculture systems in China and to analyze the sector's overall performance and variations among different systems in terms of environmental, economic, and social sustainability. Among the three pillars of sustainability, environmental performance showed the highest variation among Chinese aquaculture systems. Oyster aquaculture outperformed the other systems in environmental and social sustainability, while carp aquaculture scored highest in economic sustainability. While there is a notable variation in the results by species, the Chinese aquaculture sector on average performed close to the global average in terms of triple bottoms of sustainability.

How best to incentivize land managers to achieve conservation goals in an economically and ecologically effective manner is a key policy question that has gained increased relevance from the setting of ambitious new global targets for biodiversity conservation. Conservation (reverse) auctions are a policy tool for improving the environmental performance of agriculture, which has become well-established in the academic literature and in policy making in the US and Australia. However, little is known about the likely response of farmers to incentives within such an auction to (1) increase spatial connectivity and (2) encourage collective participation. This paper presents the first framed field experiment with farmers as participants that examines the effects of two features of conservation policy design: joint (collective) participation by farmers and the incentivization of spatial connectivity. The experiment employs farmers in China, a country making increasing use of payments for ecosystem services to achieve a range of environmental objectives. We investigate whether auction performance-both economic and ecological-can be improved by the introduction of agglomeration bonus and joint bidding bonus mechanisms. Our empirical results suggest that, compared to a baseline spatially coordinated conservation auction, the performance of an auction with an agglomeration bonus, a joint bidding bonus, or both, is inferior on two key metrics-the environmental benefits generated and cost effectiveness realized.

This study explores a spatial piecewise approach for the hedonic valuation of the area of urban green space at different distances from a property, using a rich census dataset collected from Beijing. We explore three novel empirical strategies that improve the identification of the spatial boundary or threshold distance within which green space is capitalised into housing prices. We first delineated a series of concentric circles surrounding each property and measured the area of green space within each doughnut-shaped ring. We next estimated the hedonic price using three methods. The first is a regression spline model combined with a machine learning type of model selection procedure which objectively selects the exact location of the threshold distance that optimises the model’s predictive performance. The second is a novel matching algorithm that minimises covariate imbalance for a continuous treatment variable (i.e., the area of green space) to provide stronger causal evidence on the hedonic prices of green space at different distances. The third is a spatial difference-in-differences approach that further accounts for endogeneity bias associated with unobserved factors. For our dataset, we found that housing prices are more likely to be affected by green space within a 1 km radius.