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• The 57.00% of the Yangtze River Economic Belt area exhibited a significant increase in annual NPP. • The response of NPP on climate factors was nonlinear, with greater sensitivity to temperature. • The impact of topographic factors on NPP changes had a threshold effect. Net primary productivity (NPP) of terrestrial ecosystems serves as a crucial carbon sink, playing a significant role in mitigating climate change. This study, focusing on the Yangtze River Economic Belt (YREB), a key area for both economic development and ecological conservation in China, evaluates the temporal and spatial changes in regional NPP and its responses to climatic factors against the backdrop of global change. Utilizing long-term NPP and meteorological grid data from 1981 to 2017, we analyzed the annual change patterns of NPP and its linear and nonlinear responses to climatic factors using copula functions. Our findings indicate that 57.00 % of the YREB has experienced a significant increase in NPP, with the most substantial decreases observed in Shanghai and its surrounding areas (P < 0.05). Temperature emerged as the primary climatic driver, positively correlating with NPP changes in over half of the study area (P < 0.05). Moreover, the nonlinear response of NPP to temperature and precipitation underscores the complex interactions between these factors, revealing a sensitivity to temperature variations that surpasses that to precipitation. The study also highlights the influence of solar radiation and topographic factors, with a significant positive correlation between NPP and solar radiation observed in 15.85 % of the area (P < 0.05). The practical significance of these findings lies in their implications for regional ecological management and climate resilience strategies. The positive correlation between temperature and NPP underscores the potential for targeted temperature management to optimize carbon sequestration across the YREB. Furthermore, the identified nonlinear responses and the role of solar radiation and topography in influencing NPP offer valuable insights for the formulation of adaptive strategies that enhance ecosystem productivity and contribute to achieving carbon neutrality goals. By elucidating the dynamics of NPP in response to a range of environmental factors, this study provides a foundation for informed decision-making in ecological conservation and climate change mitigation efforts within the YREB and similar regions globally. [ABSTRACT FROM AUTHOR]

• The Carbon sink capacity of a typical ecotypic county was precisely estimated. • STARFM was employed to estimate interannual NEP at county scale. • The NEP dataset at a 30 m grid scale from 2000 to 2020 was generated. • Spatio-temporal dynamics of NEP in ecotype county were grasped. Net ecosystem productivity (NEP) serves as a pivotal metric for quantitatively elucidating the carbon sink function of terrestrial ecosystems. As a prototype county for the development of an ecological civilization in China, the quantitative estimation of the ecotypic county's ecosystem carbon sink capacity holds immense significance in comprehending the carbon cycle and facilitating the sustainable advancement of regional ecosystems. This study undertook the estimation of NEP in Wuning County from 2000 to 2020, employing a fusion of multi-source remote sensing data, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the improved Carnegie-Ames-Stanford Approach model, and the soil respiration model. Furthermore, we delved into the differences in NEP across various types of land cover. In addition, we employed the Theil-Sen Median trend analysis and Mann-Kendall test to discern the spatio-temporal trends of NEP. The findings indicated the following: (1) The downscaled NDVI generated by STARFM exhibited a remarkable consistency with Landsat NDVI overall (R 2 > 0.95, P < 0.01, 0 < RMSE < 0.1). (2) The gross NEP from 2000 to 2020 in the study area ranged from 542.78 to 720 Gg C, with a multi-year average NEP of 183.84 g C m−2 yr−1. The simulated NEP demonstrated higher accuracy when compared to the measured data (R 2 = 0.79, P < 0.01). (3) The NEP exhibited a spatial pattern characterized by lower values in the central area and higher values in the north and south. Approximately 89.60 % of the total area demonstrated an increase in NEP, with woodland acting as the primary contributor, while 4.50 % of the total area displayed a decreasing trend, predominantly due to the expansion of built-up land. (4) Notable variations in NEP existed among different types of land cover. In terms of vegetation types, the annual average NEP ranked as follows: woodland > grassland > cropland. The application of STARFM has provided valuable insights into the methodology for precise delineation of spatio-temporal dynamics of NEP at the county scale. The outcomes of this study have furnished support for implementing climate change mitigation strategies in ecologically-oriented counties and the bottom-up promotion of China's carbon peaking and carbon neutrality goals. [ABSTRACT FROM AUTHOR]

• Forests show higher temperature than croplands by 0.15 ± 0.06 ℃. • Forests show higher temperature than grasslands by 0.32 ± 0.04 ℃. • Non-growing season warming effects dominate annual positive temperature response. • The biophysical effects of forest transitions vary among forest types. • Radiative processes dominate the annual net warming effect on surface temperature. Forest cover change directly affects the surface energy balance by altering the radiative and non-radiative properties of the surface. These competing processes may yield considerable biophysical impacts on local and regional climate that depend on background climate and specific forest type. Here we compared the differences in land surface temperature (LST) between temperate forests and adjacent croplands/grasslands to quantify the potential biophysical effects of transitions from croplands/grasslands to forests in Northeast China, based on multiple satellite observation products from 2001 to 2016. Forests were found to show a slight lower daytime LST and strong higher nighttime LST than adjacent croplands or grasslands. As a result, the daily LST of forests was 0.15 ± 0.06 ℃ and 0.32 ± 0.04 ℃ higher than that of croplands and grasslands, respectively. On the seasonal scale, the warming effect of cropland/grassland-to-forest transition during non-growing season outweighed the cooling effect during growing season, and dominated the annual positive LST response. Moreover, the cooling effect of cropland-to-forest transition during growing season was stronger in the early growth stage than late growth stage (−1.65 ± 0.9 ℃ in May to June, −0.39 ± 0.5 ℃ in July to September). The net cooling effect of warm temperate broadleaved forests was found to shift toward net warming effect of cold temperate coniferous forests with increasing latitude. The daytime cooling effect of forests was mainly driven by increased non-radiative evapotranspiration. Overall, the radiative process related to the solar energy dominated the net warming effect of forest transitions over Northeast China. Estimating the climate feedback potential of forests could be useful to provide theoretical references for the adaptation and mitigation strategies for climate change. [ABSTRACT FROM AUTHOR]

• The leaf area index (LAI) increased significantly in all climate zones of China. • Climate becomes wetter in A and SA areas, and soil becomes drier in SH and H areas. • Compared to LAI, the effect of climate on soil moisture (SM) was relatively strong. • The effect of LAI on SM was reversed with the change of vegetation greenness. • There are thresholds that switch the direction of vegetation influence on SM. Wetting and drying climate sequences and anthropogenic vegetation changes can have profound effects on the hydrological cycle and sustainability of terrestrial ecosystems. China contains many different climates, from humid to extremely arid and it contributes to the global mitigation strategies of climate change. China is also an important contributor to global greening and carbon sequestration. In view of this, we estimated the spatial and temporal variation of leaf area index (LAI), dryness/wetness index (DWI), and soil moisture (SM) in arid (A), semi-arid (SA), semi-humid (SH), and humid (H) climate zones during 1981–2018 in China and analyzed the patterns of DWI and LAI effects on SM under different vegetation greenness. The results showed a significantly increasing trend of LAI in all climatic zones, and the contribution was from highest to lowest: SH (8.17 × 10-3 m2 m−2 year−1), SA (7 × 10-3 m2 m−2 year−1), H (4.71 × 10-3 m2 m−2 year−1), and A (3.98 × 10-3 m2 m−2 year−1). The SA and A zones are becoming wetter, while the soil of SH and H zones are drying. The dry/wet climate variation plays a decisive role in soil moisture, while the role of vegetation is limited. Additionally, the LAI 3 (mean LAI from 2016 to 2018) thresholds were 0.25–0.41 and 0.84–0.86 for A and SA as a whole, respectively, as well as 0.47–0.54 and 0.93–1.18 for grassland, respectively. When vegetation exceeded this threshold, the effect of vegetation on soil moisture showed a shift from increasing to decreasing change. Overall, the results of the study provide improved understanding of the atmosphere-soil-vegetation interactions under climate change, as well as effects of vegetation restoration and water conservation efforts. [ABSTRACT FROM AUTHOR]

• Carbon efficiency and abatement costs of the PLEEZ are estimated. • The regional heterogeneity among the three major types of functional zones in the PLEEZ are analyzed. • Major agriculture production zone has a higher carbon efficiency and lowest abatement costs in the PLEEZ. • The global non-radial directional distance function and its dual model are used. In this paper we investigate the carbon efficiency and abatement cost for different main functional regions in China, to test whether major agriculture production zone has higher carbon efficiency and abatement cost than other functional regions. For this purpose, we use a global non-radial distance function to examine the efficiency and abatement cost of carbon emissions and take China's Poyang Lake Ecological Economics Zone (PLEEZ), one of China's national strategy for sustainable development, as an empirical study. By using the non-radial efficiency model, the carbon emissions efficiency indicator can be constructed. By employing the dual model of the non-radial directional distance function, the shadow prices of carbon emissions, which is interpreted as marginal abatement cost can be derived. County level data from 2009 to 2013 of the PLEEZ is used as the empirical investigation. Empirical results show that the CO 2 emissions of the PLEEZ are not performing efficiently and the CO 2 emissions efficiency remains at a very low level with an average value of 0.279. Meanwhile, the shadow prices of CO 2 emissions are calculated, with a mean value of 11.867 yuan per ton, which is relatively low compared with other previous studies of different regions or industry and indicate that carbon emissions are not strictly regulated. Moreover, significant heterogeneities in the carbon efficiency and abatement cost are found among three major functional zones in the PLEEZ remain. Major agriculture production zone shows better carbon efficiency than key ecological function zone, but lower than key economic development zone. It is also found carbon abatement cost of major agriculture production zone is lower than key economic development zone indicating the less pressure under climate change mitigation. [ABSTRACT FROM AUTHOR]

• A high-resolution integrated assessment framework for estimating future watershed ecosystem services was proposed under the latest SSP-RCP scenarios; • The spatial–temporal dynamics on water-related ESs of the PRB were projected under the SSP-RCP scenarios; • The main driving factors affecting the spatial–temporal heterogeneity of water-related ESs of the PRB were identified under the SSP-RCP scenarios. The projection of future ecosystem service (ES) dynamics under SSP-RCP scenarios would be beneficial for the development of effective climate mitigation strategies for policymakers. However, current projection frameworks generally neglect the downscaling of high-resolution climate and land use data, leading to uncertainties in the simulation of ESs under the SSP-RCP scenarios. To address this issue, this study proposes an integrated analysis framework to downscale climate and land use data. We first identified areas of high risk in terms of inadequate water-related ESs in the Pearl River Basin (PRB) of China. Subsequently, we analyzed key socio-environmental factors driving the dynamics of water-related ESs under future SSP-RCP scenarios. The results indicate that 1) water-related ESs in the PRB are expected to show an overall increasing trend, 2) the sub-watersheds located in the downstream is at a higher risk of experiencing insufficient supply capacity for water-related ESs, and 3) under the coupling effects of climate change, land use change, and socio-economic development, drastic changes in future land use are expected to have a stronger impact on watershed ESs than the climate (i.e., global warming). Our work provides valuable insights for policymakers and managers regarding future water and land resource planning. The proposed framework can be integrated with other hydrological or land surface models to project the spatial–temporal evolution characteristics of future watershed ESs with finer temporal resolution. [ABSTRACT FROM AUTHOR]

[Display omitted] • Modeled changes in GPP in China over the last 40 years. • Discovered an increase in GPP with a near 3-years periodic oscillation. • A recent 20 years accelerated increasing rate than that of previous 20 years. • Minimum temperature was found as the dominant factor for accelerated growth. Vegetation response to climate change can be characterized as long-term trends, but with periodic oscillations. Revealing vegetation response trends, with oscillations, is essential for formulating climate change mitigation and adaptation planning, as well as adopting ecological protection and restoration measures. Here, the long-term trend and periodic oscillations in vegetation changes were explored through a measure of gross primary production (GPP), an indicator of vegetation photosynthesis, using data of both high spatial resolution (1 km2) and a long-term of nearly 4 decades, from 1980 to 2018. The GPP data were estimated for China's terrestrial ecosystem and validated against the eddy covariance-based observations of 70 site-year in China and compared to other GPP data products. The results showed the estimated GPP can explain 76% of spatial–temporal variance with the same data accuracy as other products, but with the advantage of both high spatial resolution and long time periods that other products do not have. From the estimated GPP data, Chinese terrestrial vegetation growth was found to have a periodic oscillation of close to 2.79 years and a long-term increasing trend of 59.8 Mt C per year over 92% of all vegetated land in China from 1980 to 2018. Moreover, it was found that vegetation growth accelerated significantly in the two recent decades over the previous two decades. The growth rate was 83.5 Mt C a−1 from 2000 to 2018, which was 2.31 times faster than the rate from 1980 to 2000. The underlying mechanism of the vegetation responses was analyzed by ridge regression, a method to decrease the effects of multicollinearity. Long-term variability and trends in vegetation could be attributed to a warming minimum air temperature when examined with precipitation, maximum air temperature, and solar shortwave radiation. Though more influences, such as those from freezing and thawing and human activities, should be explored in the future, this study offers insight that warming, with increasing daily minimum temperatures, is likely to be a vital contributor to ecosystem changes, and will be essential knowledge for mitigation of effects and adaptation to future climate. [ABSTRACT FROM AUTHOR]

• We quantified wood substitution benefits in the furniture sector for China. • Displacement factor is used to assess substitution effects on emission reduction. • The avoided emissions for the products were weighted by consumption statistics. • Each tC in wood used for material substitution reduces 1.46 tC of emissions. Wood and wood-based products have positive effects on climate change mitigation. These products can substitute for energy-intensive products made of conventional materials. The main objective of this study was to quantify substitution benefits of wood furniture for China via national-scale mitigation analyses. We selected a basket of representative furniture and estimated the emission reduction for two functionally equivalent products, which differentiate in wood intensity. The emissions avoided in each type of product were weighted by its share of the furniture market to ensure that the basket can represent the current situation. Then, avoided emissions were estimated for wood commodities contained in the products basket. Results demonstrated that wood materials exerted low environmental impacts. From the less to the more wood intensity products with the equivalent function, the proportion of energy-intensive materials decreased 24% while the proportion of their greenhouse gas (GHG) emissions decreased 34%. The overall displacement factor for the wood material was 2.67 kg CO 2 eq/kg or 1.46 tC/tC. The findings highlight the importance of material selection in mitigating climate change in the furniture sector. It is urgent to use of the existing timber resources efficiently and pursue alternative schemes for emission reduction to achieve the goal of GHG mitigation. [ABSTRACT FROM AUTHOR]

The cooling effect of greenspaces is an important ecosystem service, essential for mitigating the urban heat island (UHI) effect and thus increasing urban resilience to climate change. Techniques based on landscape planning to alleviate the increasing frequency of extreme climate are becoming more of a focus in urban ecology studies. In this paper, we proposed and defined the urban cooling island (UCI) extent, intensity, and efficiency, as well as the threshold value of efficiency (TVoE) introduced from the “law of diminishing marginal utility” for the first time. The radiative transfer equation has been compared with other algorithms and used to retrieve accurate land surface temperature (LST) in a subtropical city of China − Fuzhou. Two important and arguable factor − size and shape of greenspaces also been expressed and explored. The results indicate that: (1) larger-sized greenspaces produce a higher cooling effect. However, there exist a TVoE, which is in line with our hypothesis. The TVoE in Fuzhou is 4.55 ha. (2) The circles and squares greenspaces have a significant correlation with LST and also show the highest UCI intensity and efficiency. (3) 92% of the maximum extent of greenspaces are within the 30–180 m limit, and the mean UCI extent and intensity are 104 m and 1.78 °C. (4) The greenspaces connected with waterbodies intensified the UCI effects, whereas the grassland-based greenspace shows the weakest UCI effects. The methodology and results of this study could help urban planners to mitigate the UHI effects efficiently, and to employ the climate adaptive planning. [ABSTRACT FROM AUTHOR]

[Display omitted] • TUEEC in 10 urban agglomerations in China are substantially different. • TUEEC within urban agglomeration have an impact on CO 2 emissions. • Ecological construction partially mitigated emissions growth. • Guarding against unsustainable development is vital to carbon neutral. Urban agglomerations are major generators of CO 2 emissions owing to human socioeconomic activities. Evaluating the trade-offs between urban expansion and ecological construction during rapid urbanization is a crucial step towards climate change mitigation and carbon neutrality. This study investigated the impacts of trade-offs between urban expansion and ecological construction on CO 2 emissions using multi-source data and spatial and statistical analysis. Ten urban agglomerations in China, with various development levels, were selected as study areas. The results indicated that (1) urban development in the 10 urban agglomerations expanded by 90%–288.9%, while ecological land changed by −28.1–2.3%. (2) CO 2 emissions grew by 154.2–291.2% in urban agglomerations, and the south and east of China emitted more CO 2. The high-value clusters of CO 2 inside of all urban agglomerations expanded outward from cities. (3) Urban expansion increased emissions, while ecological construction partially mitigated emissions growth. Moreover, ecological development within cities helps to reduce excessive urban emissions and should be given more attention. (4) It is as important to guard against unsustainable development as it is to reduce existing high emissions. Low-carbon development paths must continue to be supported in western China. On the basis of the results, we identify important implications for meeting China's target of carbon neutrality. These contribute to an understanding of future development modes for urban agglomerations and reveal the impact of trade-offs of land use on CO 2 emissions. [ABSTRACT FROM AUTHOR]

• Climate-sensitive additive stand biomass models were developed. • The difference in biomass estimations when using the new models at a large scale was quantified. • Forecast combination method reduced the uncertainty from the biomass models. • About $8.3 million carbon services were underestimated using traditional models. • Climate-sensitive stand biomass models were recommended for carbon service accounting. Accurate forest carbon service accounting is essential for climate change mitigation. At present, the knowledge about whether and how climate-sensitive stand biomass models could reduce the uncertainty of forest biomass/carbon estimation is lacking. Hence, the aim of this study is to develop climate-sensitive stand biomass models and quantify their differences. Data containing 539 sample plots of larch plantations in northern and northeastern China were utilized to develop two basic and the corresponding climate-sensitive stand biomass models. Owing to the uncertainty from predictors, the forecast combination method was used to combine the two basic models (FCMs) and the two climate-sensitive models (CS-FCMs) and to quantify the difference in biomass estimations at the plot and regional scales. Results showed that the adjusted R 2 values of the stand biomass models were greater than 0.85 and the relative root mean square errors ranged from 5.51% to 22.62%. The CS-FCMs produced more accurate biomass estimates than the FCMs. The difference in biomass estimations derived from biomass models with and without climatic variables was 411,549 tons or 0.27% at the regional scale, but the carbon value difference amounted to about $8.3 million. This study underlined the importance of accurate carbon accounting based on climate-modified stand biomass models for forest carbon management. [ABSTRACT FROM AUTHOR]