Zhiwen Gong

Rural development in the Global South requires governance frameworks that bridge the sectoral divides between community-based rural institutions amid compounding socio-economic and climate pressures. Community forestry (CF) and agricultural cooperatives (AC) are two such foundational institutions that operate in institutional silos despite their functional interdependence. Focusing on Nepal through Ostrom’s polycentric governance theory, this study explores:(1) How can the governance structures of CF and AC be analysed through a polycentric lens? (2) What institutional, legal, economic, and socio-cultural factors enable or constrain their collaboration? (3) What models effectively integrate them to strengthen rural resilience? A narrative synthesis of 107 peer-reviewed and grey literature sources (screened from 886 records, spanning 1990-2026) reveals a critical governance paradox: the robust legal foundations for legal collaboration exist, yet a compound governance deficit-fragmented vertical linkages, elite capture, and capacity constraints-prevents operational integration. Building on this diagnostic framework, the study develops a novel conceptualization demonstrating that CF-AC integration improves co-managed resource management, enhances livelihood diversification and climate resilience, and promotes inclusive decision-making. To operationalize these synergies, policymakers should develop integrated governance frameworks that incentivize collaboration through financial support and capacity-building, driving both ecological conservation and agricultural productivity. This study makes three core contributions: (1) theoretically extending polycentric governance to the underexplored CF-AC nexus; (2) analytically combining Ostrom’s design principles with cross-scale analysis to diagnose barriers; and (3) practically proposing three implementable governance models that preserve institutional autonomy, offering transferable insights for developing countries facing coupled socio-ecological challenges.

Land degradation continues to threaten rural livelihoods and long‐term development, boosting interest in restoration strategies such as planting fast‐growing tree species. Eucalyptus plantations have expanded rapidly in many degraded landscapes, yet evidence remains limited on how they influence household wellbeing when the economic value of tree stands is fully accounted for. This study aims to estimate the causal effects of eucalyptus planting on rural households' annual income and accumulated asset values, with explicit attention to its role in rehabilitating degraded land. We integrate the value of eucalyptus stands into household asset portfolios using the Faustmann immature stand valuation model and estimate causal impacts using propensity score matching (PSM), inverse probability weighted regression adjustment (IPWRA), and augmented inverse propensity weighting (AIPW). The novelty of this study lies in (i) incorporating tree‐stand valuation into household asset measurement and (ii) applying a doubly‐robust causal inference framework that strengthens the reliability of impact estimates. Results reveal that eucalyptus planting has a negative average impact on annual household income, primarily due to trade‐offs with farm‐based income sources. Whilst it has a positive effect on accumulated wealth. Mechanism analysis results indicate that eucalyptus planting indirectly increases household income through asset accumulation. Overall, eucalyptus cultivation, when targeted to degraded land and supported by short‐term subsidies or policy interventions, has strong potential for promoting sustainable rural development. Future research using panel and landscape‐scale ecological data would further strengthen and extend our findings for long‐term socio‐ecological outcomes of planting eucalyptus.

Rapid urbanization and economic growth in Ethiopia are transforming rural landscapes and driving an increased demand for wood resources. In response, smallholder‐managed eucalyptus‐based agroforestry (EBAF) has expanded, reshaping land‐use patterns and tree cover outside forests. This study examines the spatial interactions and spillover effects of socioeconomic and climatic drivers on EBAF expansion across 35 districts in Ethiopia from 1990 to 2024, with natural forest cover, normalized difference vegetation index (NDVI), and ecosystem service values (ESV) analyzed as complementary outcomes. Using remote sensing data and spatial econometric models, we find strong spatial dependence (0.599) in EBAF. Population density (0.0001), elevation, and mean annual temperature (0.332) significantly promote EBAF expansion, while rainfall and urbanization exert opposite effects. Climate spillovers, particularly rising maximum temperatures (−0.784), hurt dense natural forests. NDVI and ESV hotspots are concentrated in the southern highlands; degradation in northern districts is linked to declining rainfall and economic pressure. As a robustness check, we incorporated year‐specific Gi* hotspot indicators and alternative spatial weight matrices into the spatial econometric models. The results confirm that the estimated spatial spillovers remain stable and are consistent with the rook contiguity matrix. Overall, the results indicate that EBAF expansion is the dominant land‐use response to demographic, urban, and climatic pressures, with broader implications for vegetation condition and ecosystem services. Incorporating spatial spillover effects into Ethiopia's Climate‐Resilient Green Economy and Green Legacy initiatives can improve the targeting of agroforestry incentives, reforestation efforts, and ecosystem restoration, supporting both livelihood resilience and long‐term environmental sustainability.

Land use change is a critical factor influencing regional carbon emissions, and understanding its spatiotemporal variability is essential for supporting science-based emission-reduction strategies. In this study, we constructed an improved measurement framework by integrating high-resolution land use data, gridded anthropogenic carbon emission data, multi-source remote sensing indicators, and socioeconomic variables to quantify land use carbon emissions (LUCEs) in the Chengdu–Chongqing Urban Agglomeration (CCUA) from 2000 to 2022. We analyzed the temporal trends and spatial clustering of carbon emissions using the Mann–Kendall (MK) trend test and global/local Moran’s I statistics, and further explored the driving mechanisms through the Geodetector (GD) model, including both single-factor explanatory power and two-factor interaction effects. The results show that total LUCEs in the CCEC increased continuously during the study period, with significant spatial clustering characterized by high–high emission hotspots in the core areas of Chengdu and Chongqing and low–low clusters in western mountainous regions. Socioeconomic factors played a dominant role in shaping emission patterns, with construction land proportion, nighttime light intensity, and population density identified as the strongest drivers. Interaction detection revealed nonlinear enhancement effects among key socioeconomic variables, indicating an increasing spatial lock-in of human activities on carbon emissions. These findings provide scientific evidence for optimizing land use structure and formulating region-specific low-carbon development policies in rapidly urbanizing megaregions.

Introduction: An analysis of the impacts of watershed rehabilitation on land cover dynamics and income contribution for local communities is vital for assessing the sustainability of ecological restoration policy development and mitigating the adverse effects of ecosystem degradation. However, integrating ecological, economic, and social frameworks to assess rehabilitation impacts is an area often overlooked in prior studies. Objectives: This study evaluates the impacts of watershed rehabilitation interventions on land cover and ecosystem service values, and examines the challenges to the sustainability of rehabilitated watersheds in Wera district, Ethiopia. Methods: Cross‐sectional data and satellite imagery from 1984 to 2023 were applied to assess land cover change and estimate ecosystem service values. Land cover was examined for 2023 with the Landsat 8 Operational Land Imager and for 1984 with the Landsat 4–5 Thematic Mapper. Results: The results show a significant decline in degraded land and a 1554.9 ha increase in forest cover from 1984 to 2023. Ecosystem service value rose by U.S.$0.35 million, and a resilient community network has developed. However, weak institutions, low youth engagement, and limited cooperation between upstream and downstream communities remain key challenges to sustainability. Conclusions: Rehabilitation intervention boosts forest cover and economic outcomes by providing key resources. Forest cover increased by 1553.9 ha, raising ecosystem service values from U.S.$7.998 to U.S.$8.348 million.