Xia Li

1. The United Nations (UN) established the Sustainable Development Goals (SDGs) to address urgent global resource and environmental challenges (United Nations 2015). The SDGs comprises 17 goals, 16...

The consumption-based carbon emissions dataset for deforestation provides an important perspective for developing effective emission mitigation policies. However, existing datasets are largely limited to national-scale and lack spatially explicit, high-resolution data at the global level. Here, we present a global, gridded dataset of consumption-based deforestation carbon emissions at 1 km resolution for 2001–2020. The dataset integrates spatial information on road networks, deforestation, and forest carbon fluxes with country-level global trade statistics. Our dataset shows that trade-related deforestation emissions amount to 39.6 Gt CO₂e, constituting 31.8 % of global deforestation emissions. This new dataset fills a critical gap in spatially explicit consumption-based deforestation emissions data, and supports the development of targeted mitigation strategies from the consumer perspective. It also provides a valuable input for climate and carbon cycle models to assess the contribution of consumption-driven deforestation to global warming. Datasets are available at https://doi.org/10.6084/m9.figshare.28091879 (Tang et al., 2024).

Increasing human consumption has accelerated biodiversity loss. Understanding how consumption-based biodiversity impacts (biodiversity footprints) evolve is critical for mitigating this loss. This study developed a framework to project biodiversity footprints by 2050 under alternative Shared Socio-economic Pathways (SSPs) at the subnational level to inform effecitive policy interventions.We combined Multi-region Input-Output analysis, econometric modeling, and land-use simulation to project future biodiversity footprints based on socio-economic and land-use dynamics. We found that population growth, agricultural land contraction, and urbanization are the primary drivers of historical changes in biodiversity footprints. Biodiversity footprints are projected to increase substantially by 2050, with the largest rise under SSP3 (29.5%) and the lowest under SSP1 (13.4%) and SSP5 (10.4%), while decoupling from GDP growth, particularly in lower-income provinces. These findings highlight the potential for sustainably promoting economic growth through differentiated subnational strategies to mitigate biodiversity risks, especially in low-income provinces.

Carbon emissions from land use change (LUC) play a critical role in the global carbon budget and serve as a fundamental basis for national land‐use planning. However, existing estimates are typically derived from direct human activities, while the environmental indirect effects driven by both natural and anthropogenic factors are often overlooked. To date, no studies have explicitly differentiated between the direct and environmental indirect effects of interannual LUC. This omission may bias estimates of LUC‐induced carbon emissions and undermine the effectiveness of carbon reduction policies. Here, we proposed a novel accounting method that employed a process‐based dynamic vegetation model to quantify interannual LUC‐induced carbon fluxes (LUCF) in China from 1990 to 2020, further distinguishing them into direct (LUCF‐d) and environmental indirect fluxes (LUCF‐ind). The results indicate that China's overall LUCF from 1990 to 2020 functioned as a carbon source, with a trend shifting from a source in the 1990s to a sink in the past decade. Over the past 30 years, LUCF‐ind sequestered 1.39 ± 1.05 TgC yr ⁻¹ , offsetting approximately 25% of the emissions from LUCF‐d. The South and Central regions are the primary areas for the trade‐off between LUCF‐d and LUCF‐ind, with indirect environmental effects reducing direct emissions by 13.3% and 13.7%, respectively. This study aims to enhance the effectiveness of national‐level land use carbon reduction policies by integrating environmental indirect effects and providing methodological references for other countries.

Physiological heat strain induced by extreme temperature in cities has led to significant heat-related deaths. Although socioeconomic adaptation is suggested to mitigate this issue, its effectiveness is limited. Conversely, there is a lack of comprehensive evaluation on the effectiveness of landscape level strategies for mitigating heat-related deaths. We developed a comprehensive modeling framework to estimate the impacts of environmental stresses and mitigating strategies on heat-related deaths in China's cities from 2016 to 2055. The framework assesses future heat-related deaths through five experiments considering the influences of climate change, urbanization, socioeconomic adaptation, and landscape level strategies. We used extrapolated region-specific exposure-response functions (ERF) and recent advancement of geo-statistics for public health to generate urban patch level ERF curves. We used these curves, temperature and population data to generate future heat-related deaths with 1 km resolution and conducted 5,000 Monte Carlo simulations for uncertainty analysis. Our analyses estimated that heat-related mortality will increase from 136.5±16.5 deaths per million in 2016 to 175.7±27.5 deaths per million in 2055 under SSP2-RCP4.5 (shared socioeconomic pathways-representative concentration pathways) scenario and from 140.0±21.4 deaths per million to 230.2±38.7 deaths per million under SSP5-RCP8.5 scenario, despite socioeconomic adaptation and landscape level strategies. Socioeconomic adaptation (reducing deaths by 18.4-64.1 per million) and landscape level strategies (reducing deaths by 45.6-51.3 per million) significantly mitigate heat-related deaths with varying effectiveness across different income levels. Specifically, in high-income cities with dense populations, landscape level strategies are 2.2-4.3 times more effective than socioeconomic adaptation. Within these cities, implementing the same landscape level strategies in the high-density urban centers lead to an additional reduction up to 4.9-6.8 death•km^-2 compared to surrounding areas. Our framework helps to systematically understand the effectiveness of landscape level strategies in reducing heat-related mortality. Future sustainable city management should prioritize landscape level strategies along with socioeconomic adaptation to support healthy and comfortable communities. https://doi.org/10.1289/EHP15010.

In the current efforts to address global climate change, terrestrial carbon storage is a significant source of carbon sinks. Moreover, with the progress of urbanization, changes in land cover have led to increasingly unavoidable changes in terrestrial carbon storage. However, there is still a lack of research on the impact of land cover change on global terrestrial carbon storage under the latest IPCC scenarios. Therefore, this study utilizes the InVEST model to examine the impact of future land dynamics on global terrestrial carbon storage under various SSP‐RCP scenarios from 2015 to 2100. The results reveal significant regional differences and trends. For instance, under the SSP4‐3.4 scenario, global terrestrial carbon storage loss could reach up to 30.2 PgC due to land conversions, such as the transformation of tropical rainforests into bioenergy crops in Indonesia. The social cost of offsetting terrestrial carbon loss could range from trillions to tens of trillions of dollars in various scenarios. Additionally, our analysis indicates that urban expansion generally reduces carbon storage, but strategic urban planning can mitigate this impact. In ecological protected areas and biodiversity hotspots, we observed a polarized trend: areas with increasing carbon storage are experiencing growth at a faster rate than historically, while areas with decreasing carbon storage are facing accelerated losses. This disparity underscores the necessity for targeted conservation efforts. In conclusion, this study highlights the importance of sound land management and policy interventions in mitigating terrestrial carbon storage losses, protecting ecologically sensitive areas, and achieving climate goals.