Regional Carbon Research Articles

Can New Quality Productivity Promote the Carbon Emission Performance—Empirical Evidence from China

New quality productivity (NQP) has the possibility to enhance carbon emission performance which will fortify the groundwork for long-term economic expansion even further. The research examines the panel data of 30 provinces spanning the years 2012 to 2022 for an evaluation framework for NQP and carbon emission performance at the provincial level. Employing fixed effect models, mediation effect analysis, and spatial econometrics, the study explores the effect of NQP on carbon emission performance, its mediating mechanisms, and the spatial spillover effects. The findings indicate that (1) NQP significantly lowers carbon emissions for every unit of GDP and enhances carbon emission performance, and the result holds up when the instrumental variable methods are used. (2) The NQP had a significant contribution to improving carbon emission performance via advancements in green innovation. (3) The NQP does more than directly enhance the regional carbon emission performance; in contrast, it additionally positively influences the carbon emission performance level of the adjacent regions by the spatial spillover effect. (4) The impact of NQP on carbon emission performance is particularly pronounced in eastern and innovative regions. On this basis, we should vigorously develop the NQP, strengthen cross-regional policy coordination, and promote green and sustainable development.

Identifying Optimal Variables to Predict Soil Organic Carbon in Sandy, Saline, and Black Soil Regions: Remote Sensing, Terrain, or Climate Factors?

Environmental variables have a substantial effect on the reliability of soil organic carbon (SOC) mapping. However, it is still challenging to identify which environmental variables are effective in cropland SOC prediction in sandy, saline, and black soil regions. To address this issue, we used the principal component analysis (PCA) method for the feature selection of bands, spectral indexes, and terrain factors for each region. Based on the selection feature, we used global RF and local RF for SOC prediction for these three regions. Our results indicated that (1) climate factors, particularly mean annual precipitation and mean annual temperature, were the most effective predictors in SOC mapping across sandy, saline, and black soil regions, as indicated by their significant contribution to RF model performance (R2 > 0.63); (2) followed by climate factors, the Transformed Vegetation Index (TVI) was consistently identified as the most influential variable for SOC prediction among spectral indexes in all three regions; (3) a local regression method based on RF models showed good performance compared to a global model; (4) desertification and salinization were the main reasons for the spatial differences in AH and DM&LD, respectively. The SOC of HL in black soil regions was consistent with the climate change trend because of the latitude difference. This study provides valuable information for constructing a more precise soil prediction strategy for cultivated land in sandy, saline, and black soil regions.

The contribution of small and medium diameter trees in West African Savanna to regional biomass and carbon pools

The contribution of small and medium diameter trees in West African Savanna to regional biomass and carbon pools

Spatial-temporal characteristics and influencing factors of farmland carbon emissions in Guangdong Province, China

Agricultural carbon emissions account for 17% of total greenhouse gas emissions in China. To effectively address the eco-environment changes in farmland, which serves as the foundation of agricultural activities, it is essential to estimate regional farmland carbon emissions. This study calculated the farmland carbon emissions in Guangdong from 2011 to 2021 using the classical IPCC carbon emission calculation methodology. The decoupling characteristics betweem farmland carbon emissions and agricultural output values were analyzed utilizing a decoupling model, and the influencing factors were examined through the Logarithmic Mean Divisia Index (LMDI). The results indicate that: 1) Farmland carbon emissions in Guangdong decreased by 13.21% from 2011 to 2021, with pesticide reductions contributing the most to emission decreases. Chemical fertilizers were the largest contributor to farmland carbon emissions, accounting for approximately 61.78% of the total. 2) The spatial distribution of farmland carbon emissions followed the pattern of “Western Guangdong > Northern Guangdong > Eastern Guangdong > Pearl River Delta”. While emission intensity generally declined, regional disparities widened. 3) Most cities in Guangdong exhibited a strong decoupling relationship between farmland carbon emissions and agricultural output values, with decoupling coefficient ranging from −1.182 to −0.004. However, Heyuan and Shenzhen demonstrated a weak decoupling relationship. 4) The primary driver of increased farmland carbon emissions in Guangdong was the level of agricultural output, while improvements in agricultural production efficiency were the most significant inhibitory factor, followed by changes in the scale of agricultural labor force. This study offers policy recommendations to promote the reduction and sequestration of farmland carbon emissions in Guangdong.

Characteristics and Influencing Factors of Soil Particulate and Mineral-associated Organic Carbon in Cropland Soil across Sichuan Basin

Exploring the composition of regional soil organic carbon （SOC） components and identifying their influencing factors are of utmost importance to deeply understand the potential mechanisms of SOC change in cropland soil. Based on data from 871 soil sampling points, this study explored the characteristics of soil particulate and mineral-associated organic carbon （POC and MAOC） in the surface soil of cropland and the relationships with climate, terrain, soil texture, agricultural land-use type, and fertilization across the Sichuan basin using analysis of variance, correlation analysis, and a random forest model. The results showed that the average content of POC and MAOC in the surface soil of cropland was 5.01 g·kg-1 and 9.79 g·kg-1, respectively, accounting for 32.5% and 67.5% of the total SOC； the proportion of POC increased with SOC. The two SOC fractions showed a similar spatial distribution pattern, with the content being higher in peripheral areas and lower in the middle of the basin, which were determined together by structural and random factors. The results of the random forest model showed that the relative importance of agricultural land-use type was much more important than other factors. NPP, mean annual temperature, and soil texture showed the largest correlations with the two SOC fractions in dryland； mean annual temperature and slope had larger correlations with POC and mean annual temperature and soil texture in the dryland-paddy rotation land； and soil texture was the most relevant factor to POC and MAOC in the paddy field. The above results suggested that MAOC was the main component of SOC, and agricultural land-use type was the dominant control and regulated the effects of other factors on the two SOC fractions.

Emerging carbon dioxide hotspots in East Asia identified by a top-down inventory

A high-resolution carbon dioxide emission inventory is essential for accurate carbon emission assessment and management. This study used a top-down spatial proxy model to develop a high-resolution (1 km) carbon dioxide emission inventory for East Asia from 2012 to 2021. Combining multiple data sources with geographically weighted regressions can improve the accuracy of the spatial distribution of sectors within carbon dioxide inventories. To demonstrate accuracy, we compared our inventory with existing carbon dioxide inventories, such as the Multi-resolution Emission Inventory for China, which uses a ‘bottom-up’ approach. We outline and discuss the convergence and divergence in the estimation results of the different approaches, finding generally consistent results. Based on the high-resolution carbon dioxide grid, nationally, China accounted for over 80% of East Asia’s carbon dioxide emissions, followed by Japan and South Korea. The results of the hotspot and driver analyses reveal persistent hotspots in Northern and Eastern China, as well as new trends in Northwestern China. The main drivers of emissions growth in hotspots are economic growth and changes in the energy mix. The drivers of emission changes in cold hotspot regions vary depending on regional characteristics. Actionable insights are provided for targeted regional carbon reduction strategies.

The spatiotemporal evolution of carbon storage driven by land use changes in Jinan City over the period 2000–2019

ABSTRACT Analyzing the temporal and spatial dynamics of land use change and carbon storage can advance our understanding of regional carbon cycling, facilitate the transition of carbon sources, and contribute to climate change mitigation efforts. In this study, we utilized Landsat data and the carbon density correction methodology to explore land use dynamics and carbon storage variations in Jinan City from 2000 to 2019. The results show that: (1) The main land use types in Jinan City are forest, cultivated land, and construction land, with approximately 29.57% of the land undergoing changes over the past 20 years. (2) The total carbon storage of land use types in Jinan City in 2000, 2010, and 2019 was 33,684.4 × 104 tonnes 34,807.4 × 104 tonnes, and 32,060.3 × 104 tonnes, respectively. (3) The carbon storage in Jinan shows an overall decreasing trend, mainly due to the conversion of cultivated land and forest to construction land. The findings of this study could provide a scientific basis for ecological protection and sustainable development in the local area.

Spatiotemporal Variation and Driving Factors of Carbon Sequestration Rate in Terrestrial Ecosystems of Ningxia, China

This study investigates the spatiotemporal variation characteristics and influencing factors of an ecosystem’s carbon sequestration rate (CSR) in the Ningxia region from 2001 to 2023, providing scientific evidence for assessing the regional carbon sequestration capacity and formulating carbon neutrality policies. Based on ground observation data and multimodal datasets, the optimal machine learning model (EXT) was used to invert a 30 m high-resolution vegetation and soil carbon density dataset for Ningxia from 2000 to 2023. Annual variation analysis and geographical detector methods were employed to assess the spatiotemporal distribution characteristics of the CSR from 2001 to 2023 and identify the primary influencing factors. The results show that from 2001 to 2023, the CSR of the Ningxia ecosystem exhibits a spatial distribution pattern characterized by higher values in the south and lower values in the north, with a mean value of 21.95 gC·m−2, and an overall fluctuating increasing trend, with an annual growth rate of 0.53 gC·m−2 a−1. Significant differences in the CSR exist across different ecological regions. In terms of land use types, the ranking of carbon sequestration capacity is forest > farmland > grassland > barren, while the ranking of the carbon sequestration enhancement capacity is farmland > forest > grassland > barren. Among land use change types, the carbon sequestration enhancement capacity significantly increased when grassland was converted to forest or shrubland, farmland to forest–grassland, and bare land to forest–grassland, with increases of 42.9%, 9.2%, and 34.6%, respectively. The NDVI is the primary driver of CSR spatiotemporal variation, while the interaction between the Enhanced Vegetation Index (EVI) and soil bulk density has a more significant explanatory power for CSR spatial differentiation. This study shows that ecological restoration projects, such as the conversion of cropland to forest (or grassland) and protective farmland measures, play a significant role in enhancing the carbon sequestration capacity in Ningxia.

Green Finance and Climate Technology: Evidence From a Quasi‐Natural Experiment

ABSTRACTAddressing the productivity challenge of climate technology (ClimTECH) firms and avoiding the “green trap” is crucial for decoupling economic growth from carbon emissions and achieving sustainable development. This study uses the establishment of green finance reform and innovation pilot zones as a quasi‐natural experiment and employs a difference‐in‐differences model to explore the impact of green finance policies on the total factor productivity (TFP) of ClimTECH firms and its spillover effects. The results show that (1) Green finance policies significantly increase TFP, especially in state‐owned firms, firms that actively disclose environmental information, firms led by long‐tenure CEOs, and those in regions with strong intellectual property protection. (2) The channel analysis shows that green finance policies enhance firms' TFP by easing financing constraints, encouraging green technology improvements, and improving capital allocation efficiency. (3) The spillover effect analysis shows that the TFP increase driven by green finance policies not only enhances firm value but also stimulates local green innovation and reduces regional carbon emissions. This research offers theoretical insights and policy implications for strengthening green finance frameworks and enhancing the environmental responsibility of ClimTECH firms.

Spatiotemporal dynamics of soil organic carbon in desert region and its response to climate change: A case study of Heihe river

Spatiotemporal dynamics of soil organic carbon in desert region and its response to climate change: A case study of Heihe river

Spatial and temporal evolution patterns and spatial spillover effects of carbon emissions in China in the context of digital economy.

Global warming caused by carbon emissions has become a significant concern for countries worldwide. This study thoroughly examines the spatiotemporal patterns and spatial spillover effects of carbon emissions in China. This research employs kernel density estimation, Moran's index, and the standard deviation ellipse model to analyse the spatiotemporal evolution of carbon emissions in China while utilizing the spatial Durbin model to explore the spatial spillover effects of the digital economy on carbon emissions. The subsequent findings are derived from the following: (1) China's carbon emissions are characterized by substantial spatial and temporal agglomeration. Low carbon emissions are in the eastern littoral regions, while high carbon emissions are concentrated in the inland areas, such as the northwest. The local Moran index suggests that high-high and low-low clustering patterns characterize China's carbon emissions. (2) The spatial trends and evolutionary characteristics of carbon emissions in China are readily apparent. During the sample period, the carbon emission level in the east and west was significantly higher than in the central region, and the gap between the areas was progressively narrowing. The results of the standard deviation ellipse indicate that China's carbon emissions are undergoing a substantial discrete phenomenon in their spatial distribution. (3) Digital economies reduce carbon emissions, have regional spillover effects, and reveal geographical variance across eastern, central, and western regions. This study offers quantitative evidence for integrated nationwide and regional emission reduction and carbon mitigation strategies, as well as for region-specific emission reduction programs.

The Digital Empowerment Promotes Synergistic Efficiency in Regional Pollution Reduction and Carbon Emission Reduction—Analysis of the Moderating Effects of Market Structure and Government Behavior

The Digital Empowerment Promotes Synergistic Efficiency in Regional Pollution Reduction and Carbon Emission Reduction—Analysis of the Moderating Effects of Market Structure and Government Behavior

Internal climate variability modulates decadal changes in ocean anthropogenic carbon storage

Abstract The ocean removes man-made (anthropogenic) carbon from the atmosphere and thereby mitigates climate change. Observations from global hydrographic surveys reveal the spatial and temporal evolution of the ocean inventory of anthropogenic carbon and suggest substantial decadal variability in historical storage rates. Here, we use a 100-member ensemble of an Earth system model to investigate the influence of external forcing and internal climate variability on historical changes in ocean anthropogenic carbon storage over 1994 to 2014. Our findings reveal that the externally forced, decadal changes in storage are largest in the Atlantic (2–4 mmol m−3 decade−1) and positive nearly everywhere. Internal climate variability modulates regional ocean anthropogenic carbon storage trends by up to 10 mmol m−3 decade−1. The influence of internal climate variability on decadal storage changes is most prominent at depths of ∼300 m and at the edges of the subtropical gyres. Internal variability in anthropogenic carbon in the extratropics has high spectral power on decadal to multi-decadal timescales, indicating that the approximately decadal repetitions of hydrographic surveys may produce storage change estimates that are heavily influenced by internal climate variability.

Remotely-sensed products capture the spatial distribution of regional carbon sequestration potential ability: A case study of Hubei Province, China

Remotely-sensed products capture the spatial distribution of regional carbon sequestration potential ability: A case study of Hubei Province, China

Distribution and controlling factors of dissolved inorganic carbon in urban rivers of a North China megacity: Insights from δ13CDIC.

Transportation and transformation of the dissolved inorganic carbon (DIC) play a critical role in the regional carbon cycle. To clarify the natural and anthropogenic impacts on DIC, the concentration and isotopic composition of DIC (δ13CDIC) in two typical urban rivers in northern China (Yongding River, YDR, and Chaobai River, CBR) were measured. Mass-balanced calculations were employed to quantify the impacts of different weathering processes. The results indicated that carbonate weathering under the influence of mixed weathering agents was the primary weathering process in the YDR and CBR. Carbonate dissolution by carbonic acid was an important source of DIC in both the YDR and the CBR. According to the end-member calculations of δ13CDIC, soil-respiration CO2 was a key factor controlling the carbonate weathering by carbonic acid. A high contribution of sulfuric and nitric acids to carbonate weathering was found in the YDR, especially in downstream areas with high-level anthropogenic activities. This indicated that anthropogenic activities can profoundly impact the weathering processes in watersheds. This study provides insights into the complex interactions between human activities and natural processes in shaping the carbon dynamics of urban rivers, highlighting the importance of natural and anthropogenic factors on carbon dynamics in urban rivers and providing insights essential for predicting carbon flux changes amidst urbanization and climate change.

Optimization of Key Stand Structural Factors to Enhance Water-Holding Function, Soil Conservation, and Carbon Sequestration in Schima superba Forests: Insights from Subtropical Dongbai Mountain

Revealing the interactions between stand structure factors and ecosystem functions is crucial for enhancing forest soil and water conservation, as well as carbon storage. However, the optimal stand structure configuration for achieving superior ecological functions remains unclear, particularly in complex subtropical evergreen broad-leaved forests. Using Schima superba Gardn. et Champ. forests in Dongbai Mountain as a case study, this study investigated the effects of seven stand structure factors—stand density, canopy density, uniform angle index, and mixing degree—on three key ecosystem functions, water-holding function, soil improvement function, and carbon sequestration. Redundancy analysis (RDA) and structural equation modeling (SEM) were employed to quantify these relationships. The results identified stand density as the most influential factor, directly or indirectly affecting ecosystem functions by regulating other structural attributes. Optimal structural configurations were determined, including stand densities of 1228, 1532, and 1675 plants·hm−2 for maximizing water-holding function, soil improvement function, and carbon sequestration, respectively. Recommendations emphasize adjusting stand density, canopy density, uniform angle index, and mixing degree according to site-specific conditions. Practical strategies, such as replanting, intercropping, and introducing functionally complementary species, should aim to enrich vertical structure, maintain species randomness, and avoid dominance by a single species or excessive density. These findings offer actionable insights for improving the ecological functions of subtropical forests and have broader implications for sustainable forestry management, advancing regional carbon neutrality, and addressing global ecological challenges.

Simulation and Pathway Selection for China’s Carbon Peak: A Multi-Objective Nonlinear Dynamic Optimization Approach

This study innovatively develops a multi-objective Markal-Macro model, which simultaneously considers three objectives: minimizing carbon emissions from energy consumption, minimizing carbon emissions from production processes, and maximizing societal welfare. Based on the Cobb–Douglas production function, we construct a production function of carbon emission and use it as a coupling equation of the Markal-Macro model (Markal is the abbreviation of market allocation, and Macro is the abbreviation of macroeconomy). This enables the coupling of the endogenous variables of carbon emissions and those related to maximizing societal welfare. By collecting relevant data on energy consumption, production outputs, and key economic indicators, five different scenarios are established. To enhance the computational efficiency of the simulation, we introduce a Firefly Algorithm into the penalty function method. The objective of our simulation is to explore the optimal carbon peak pathway for China. The results indicate that under the baseline scenario, China can achieve its carbon peak by 2029, with the peak value reaching approximately 12.5 billion tons of carbon dioxide. Finally, based on the simulation results, this study provides specific policy recommendations for China’s carbon peak pathway, addressing aspects such as industrial structure, energy consumption structure, the share of clean energy, economic growth targets, and the growth of emission reduction expenditures, while considering regional five-year plans and regional carbon peak strategies. From the aspect of the practical contributions, this article not only provides a set of methods for policymakers to make the Carbon Peak Implementation Plan but also offers an optimal path to improve the sustainable development for China.

Study on soil organic carbon in permafrost regions

The Bayanzurkh soum of Khuvsgul province is located in the continuous and discontinuous permafrost zones. We investigated the soil organic carbon content in the Dood Toom, Dund Toom, Altarga and Beltes rivers valleys in Bayanzurkh Soum. In the study area, we collected 720 soil samples at 181 sites between 2022 and 2023. We determined soil organic carbon content (SOC, %), soil organic carbon density (SOCD, kg m-2), bulk density (g/cm3), stone content (%), and moisture content (%). The cryic umbrisols, cryic histosols, umbrisols, calcic histosols, sceletic regosols, and mollic cambisols soils were distributed across the study area. The result showed that the SOC content ranged between 1.7 and 93.8%, and cryic histosols was a higher values in SOC content. Whereas, SOC density ranges between 4.7 and 72.7 kg m-2, and cryic histosols was higher values in SOC density. According to our results, SOC density of cryic umbrisols and cryic histosols were a higher value than previous study. But, the SOC density of our study was lower than in permafrost regions of the Northern Hemisphere (Arctic). In addition, the SOC content had a high positive correlation with moisture content (r2 = 0.63), and highly negatively correlated with bulk density (r2 = -0.87) and stone content (r2 = -0.52). Цэвдэгт бүс нутгийн хөрсний органик нүүрстөрөгчийн судалгаа Хөвсгөл аймгийн Баянзүрх сум нь цэвдгийн үргэлжилсэн ба тасалданги тархалттай бүсэд оршдог. Бид энэхүү судалгаагаар Баянзүрх сумын Доод Тоом, Дунд Тоом, Алтарга, Бэлтэс голуудын хөндийн хөрсний органик нүүрстөрөгчийн агууламжийг судалсан. Судалгааны талбайд 2022-2023 оны хооронд нийт 181 хөрсний зүсэлт хийж, 720 орчим дээж цуглуулан хөрсний органик нүүрстөрөгчийн хэмжээ (ХОН, %), хөрсний органик нүүрстөрөгчийн агууламж (ХОН-ийн агууламж, кг м-2), эзлэхүүн жин (г/см3), чулууны агууламж (%), чийгийн хэмжээ (%) зэрэг үзүүлэлтүүдийг тодорхойлов. Судалгааны талбайд тайгын цэвдэгт, хүлэрлэг цэвдэгт, тайгын ширэгт, карбонаттай хүлэрлэг, чулуурхаг ялзмагт, уулын бараан хөрс тус тус тархсан байна. Судалгааны талбайн ХОН-ийн хэмжээ 1.7-93.8 % хооронд хэлбэлзэж хүлэрлэг цэвдэгт хөрсөнд хамгийн өндөр байсан. Харин ХОН-ийн агууламж 4.7-72.7 кг м-2-ийн хооронд хэлбэлзэж тайгын цэвдэгт хөрсөнд өндөр байсан. Судалгааны үр дүнг Монгол орны тайгын болон хүлэрлэг цэвдэгт ХОН-ийн агууламжийн өмнөх судалгаатай харьцуулахад бидний судалгааны үзүүлэлт өндөр байсан бол дэлхийн бөмбөрцгийн хойд хагасын (Арктикийн) цэвдэгт бүс нутгийн ХОН-ийн агууламжаас бага байсан. Судалгааны цэгүүд дээрх ХОН - ийн хэмжээг чулуу болон чийгийн хэмжээ, эзлэхүүн жинтэй харьцуулахад чулууны агууламж (r2 = -0.52) болон эзлэхүүн жин (r2 = -0.87) - ээс хүчтэй урвуу хамааралтай байсан бол чийгийн хэмжээ (r2 = 0.63) - ээс хүчтэй эерэг хамааралтай байна. Түлхүүр үгс: Цэвдэг, Хөрсний органик нүүрстөрөгч, Хөрсний органик нүүрстөрөгчийн агууламж, Баянзүрх сум

Analysis of the Factors Influencing Carbon Trading Prices in Shenzhen

China's carbon emissions trading market is in the ascendant. As the country with the largest carbon emissions in the world, China is actively taking measures to deal with global climate change. China has put forward the long-term goal of reaching the peak of carbon emissions by 2030, achieving carbon neutralization by 2060 and shouldering important international social responsibilities. The construction and development of carbon emissions trading market is a key link in achieving the goal of carbon peak and carbon neutralization. The research on the influencing factors of China's regional carbon emissions trading price and the development of regional green finance can provide decision support for achieving the goal of carbon peak and carbon neutralization in China. Starting from the four latitudes of domestic and foreign economies, domestic and foreign energy prices, international carbon prices, and international major currencies and RMB exchange rate, this paper selects 13 independent variables to explore their impact on the dependent variable Shenzhen carbon trading average price. First of all, this paper uses lasso for regression modeling, and selects the λ that minimizes the mse into the model. Five features of the model are screened out. Among the remaining characteristics, the CSI 300 index has a greater positive effect on the Shenzhen carbon trading price, while the SSE 50 has a greater reverse effect on the Shenzhen carbon trading price. Secondly, in order to avoid using lasso to compress the model coefficients too small, this paper uses the elastic network model to fit the data, the fitting result is actually the ridge regression model, and the prediction mean square error of the test set is greater than that of lasso modeling. Finally, considering all the characteristics comprehensively, this paper uses the random forest algorithm to model the data, and the characteristics that make a great contribution to the carbon trading price in Shenzhen are EU emission quota and Guangdong carbon trading price. and the mean square error of the test set modeled by random forest algorithm is the smallest of the three models, and the prediction effect is the best.

Assessing the impact of land use and cover change on above-ground carbon storage in subtropical forests: a case study of Zhejiang Province, China

ABSTRACT Land Use and Cover Change (LUCC) has emerged as a primary driver of terrestrial carbon storage changes. However, the contributions of LUCC to Above-Ground Carbon (AGC) storage in subtropical forests remain unclear due to the complex and diverse LUCC trajectory. Quantitative assessment of the impact of different LUCC trajectories on carbon storage is essential for regional carbon cycle mechanisms. Therefore, this study focuses on Zhejiang Province, a representative subtropical forest region in China, to accurately assess the contribution of LUCC to AGC storage changes from 1984 to 2019. We first mapped the land cover patterns using the random forest and spatiotemporal filtering algorithm and then applied these patterns to drive an optimized BIOME-BGC model to simulate the spatiotemporal distribution of AGC density. Finally, the LUCC trajectories were classified into three categories: afforestation, deforestation, and forest type transformations. Their contributions to AGC changes were isolated and analyzed through the trajectory analysis. The results demonstrated that the forest area of Zhejiang Province increased from 5.35 × 106 ha to 6.83 × 106 ha (+27.66%) and the total forest AGC storage increased from 80.52 Tg C to 124.16 Tg C (+54.19%) between 1984 and 2019. The increase in forest AGC due to LUCC amounted to 31.26 Tg C, contributing 71.63% to the total. Specifically, the afforestation, deforestation, and forest type transformations contributed 82.37%, −17.27%, and 6.53% to the change in AGC, respectively. Overall, the afforestation within the LUCC trajectories was the primary contributing factor to the growth of forest AGC in Zhejiang Province from 1984 to 2019. This study obtained accurate LUCC and AGC data, clarifying the contribution of different LUCC trajectories and providing a better understanding of the responses of the forest carbon storage to LUCC dynamics.