Regional Carbon Research Articles

From global to national GHG budgets: The REgional carbon cycle assessment and processes-3 (RECCAP3)

From global to national GHG budgets: The REgional carbon cycle assessment and processes-3 (RECCAP3)

Significant greening in the western Tibetan Plateau and surroundings occurred after the 1970s

Vegetation greening on the Tibetan Plateau (TP) has profoundly changed the connection between terrestrial ecosystems and regional carbon, water, and energy cycling, but existing research on vegetation greenness can only be traced back to the 1980s, because of the scarcity of long-term remote sensing data. Here we present an ice-core record of pollen accumulation rates with a 3-year resolution during 1935–2012 AD for the western TP. Based on backward trajectory and wind data, we identified the pollen source region and transport mechanisms. We found that the 3-year total pollen accumulation rate (TPAR3a) is a good indicator of vegetation coverage of the source region, and our data showed that the greening of the western TP and surroundings began as early as 1935 AD, mostly due to regional warming and moistening. The greening trend became more significant after the 1970s, which is mostly related to the significant increase in temperature.

Biomarker 14C evidence for sources and recycling of pre-aged organic carbon in Arctic permafrost regions

Biomarker 14C evidence for sources and recycling of pre-aged organic carbon in Arctic permafrost regions

Regional Differences, Convergence Characteristics, and Carbon Peaking Prediction of Agricultural Carbon Emissions in China

As a major agricultural country, agricultural carbon emissions in China are one of the key areas of focus for achieving China's “dual carbon” targets. This study utilizes the Dagum Gini coefficient, convergence models, and the ARIMA model to analyze the characteristics of agricultural carbon emissions in China's three major grain production functional zones. The results show that: (1) The agricultural carbon emissions in the national, main grain-production, and main grain-sales regions exhibit a fluctuating decreasing trend, while the agricultural carbon emissions in the grain production-sales balance region show a fluctuating increasing trend. The primary sources of agricultural carbon emissions are agricultural material inputs and enteric fermentation of ruminants. (2) The intra-regional differences in agricultural carbon emissions are the largest in the main grain-sales region, followed by the grain production-sales balance region, and the smallest in the main grain-production region. The contribution rate of inter-regional differences is the highest, making it the main source of overall disparity, while the contribution rate of hyperbolic density is the lowest. (3) Both the national level and the three major grain production functional zones exhibit conditional β-convergence in agricultural carbon emissions, indicating that the agricultural production in the national, main grain-production, and main grain-sales regions shows a “carbon neutral” characteristic, while the agricultural production in the grain production-sales balance region shows a “carbon peak” characteristic. (4) The agricultural carbon peak in China occurred in 2015, with a total carbon emission of 164.5 million tons. The main grain-production and main grain-sales regions have already reached carbon peak, while the grain production-sales balance region is expected to reach its carbon peak by 2030.

An integrated assessment system for regional carbon emissions: Insights into China's sustainable development

An integrated assessment system for regional carbon emissions: Insights into China's sustainable development

A multi-agent distributed electricity-carbon shared trading strategy considering regional carbon inclusion

A multi-agent distributed electricity-carbon shared trading strategy considering regional carbon inclusion

Forest fire emission estimates over South Asia using Suomi-NPP VIIRS-based thermal anomalies and emission inventory

Emission estimates of carbon-containing greenhouse gases (CO2, CH4) and aerosols (PM2.5) were made from forest fire across South Asia using Visible Infrared Imaging Radiometer Suite (VIIRS) based thermal anomalies and fire products. VIIRS 375 m I-band active fire product was selectively retrieved for the years 2012 to 2021 over forest cover across South Asia. Annual incidence of fire events across South Asia was 0.17 (±0.05) million (M) with robust spatio-temporal heterogeneity. Fire occurrences were mainly concentrated over the forest across Hindu Kush Himalayan region (HKH; 56%), Deccan Plateau (DP) and Central Highlands (CH; 34%). Monthly mean fire incidences emphasize February to May as a typical forest fire season, accounting 90% of annual fire counts. The highest fire pixel density (>1.5 km -2 yr-1) was noted over the tropical dry/moist deciduous and tropical semi-evergreen forests. Strong diurnal nature of fire radiative power (FRP) was evident with >85% of FRP linked to daytime retrieval. VIIRS based Fire Emission Inventory (VFEI, Version 0) was followed to constitute regional emissions from biomass-based fire. Forest fire accounted a yearly emission of 91.58 (±14.76) and 0.25 (±0.04) Tg yr-1 CO2 and CH4 respectively, with 25.14 (±3.94) Tg of cumulative carbon release per year, i.e., roughly 1.3% of global fire-related carbon emission. Fire associated PM2.5 emission rate was 0.60 (±0.10) Tg yr-1, 95% of which emitted during peak fire season as was the case for carbon-containing gases. Forest fire across HKH (75%) and DP+CH (20%) predominately contribute to the regional carbon emission, while also accounting 68% (HKH) and 27% (DP+CH) of fire associated PM2.5 emission budget. With >70% of forest fires within South Asia being typically anthropogenic, forest fire appears to be a major sector of greenhouse gas and aerosols emissions, and necessitate planning and strict legalities to reduce emission load.

Impact of dual-carbon attention competition from local government on regional carbon emissions in China.

Extreme climate change induced by carbon emissions has received extensive attention from governments worldwide. Strong competition in local governments' dual-carbon attention (GCA) produces an effective influence on the reduction of regional carbon emissions, confirming crucial policy implications. In this study, textual content analysis is employed to measure the GCA level and GCA competition, and the mechanism by which GCA competition reduces regional carbon emissions in China is explored from the perspective of competition behaviors. The findings demonstrate that the increase of GCA competition positively influences the reduction of regional carbon emissions. Influence mechanism analyses verify that increasing GCA competition primarily stimulates the greater reduction of regional carbon emissions by intensifying competition in energy consumption reduction and the optimization of the energy consumption structure. The influence of GCA competition on reduced regional carbon emissions is significantly related to industrial structure upgrading, the energy consumption structure, and environmental governance investment, as well as inter-government competition in these areas. The detailed findings of this research can provide economic and environmental benefits for policymakers, and can provide corporations with more targeted policy recommendations related to dual-carbon attention and carbon emission reduction.

Modelling the volatility dynamics of China's regional carbon markets: The heterogeneous effects of the fossil and clean energy electricity generation

Modelling the volatility dynamics of China's regional carbon markets: The heterogeneous effects of the fossil and clean energy electricity generation

Research on regional carbon emission scenario simulation based on GA-BP-KDE under uncertain conditions

Research on regional carbon emission scenario simulation based on GA-BP-KDE under uncertain conditions

High-speed rail network and regional carbon emissions: Carbon lock-in or unlocking?

High-speed rail network and regional carbon emissions: Carbon lock-in or unlocking?

Does digital innovation improve carbon productivity? A new perspective based on industrial upgrading

ABSTRACT In the era of digital economy, digital innovation is an important path to promote the improvement of carbon productivity in developing countries. Based on China’s experience, this paper systematically constructs a digital innovation measurement system, and takes the heterogeneity threshold of industrial transformation upgrading as an entry point, and adopts a non-linear dynamic panel measurement model to empirically explore the mechanism of industrial transformation upgrading threshold of digital innovation on regional carbon productivity. The results show that the current development level of China’s digital innovation is relatively low, with significant regional heterogeneity. In particular, the impact of digital innovation on regional carbon productivity is subject to the heterogeneous threshold effect of industrial transformation upgrading: the low degree of industrial transformation upgrading hinders the role of digital innovation in improving carbon productivity to some extent, and with the further improvement of industrial transformation upgrading, the enabling effect of digital innovation can be effectively brought into play to improve carbon productivity. That is, digital innovation and regional carbon productivity present ‘U-shaped’ relationship. This paper has clarified the differential‘new phenomena’ in the process of promoting carbon productivity, providing new insights for achieving ‘carbon reduction and economic promotion’ in developing countries.

Multi-Scenario Prediction of Dynamic Responses of the Carbon Sink Potential in Land Use/Land Cover Change in Areas with Steep Slopes

Terrestrial ecosystems are vital carbon sinks that can effectively restrain the rise in CO2 in the atmosphere. How ecosystem carbon storage (CS) in semi-arid watershed areas with slow urbanization is affected by comprehensive factors of the environment and land use, along with its temporal and spatial changes has still not been fully explored. Notably, there is a paucity of research on the temporal and spatial changes and development trends of CS in the rapid deformation belt of slopes from the eastern margin of the Qinghai–Tibet Plateau to the Loess Plateau. Taking Bailong River Basin (BRB) as an example, this study combined GeoSOS-FLUS, the InVEST model, and localized “social–economic–nature” scenario to simulate the long-term dynamic evolution of CS. The aim was to study how topographic factors and land use change, and their interactions impact carbon sinks and gradient effects in steep-slope areas, and then find out the relationship between carbon sinks and topographic factors to explore strategies to improve regional carbon sink capacity. The results showed that the following: (1) CS in BRB increased year by year, with a total increase of 558 tons (3.19%), and showed significant spatial heterogeneity, mainly due to the conversion of woodland and arable land; (2) except for land use type, the relationship between CS and topographic gradient is inverted U-shaped, showing a complex spatial response; and (3) it is estimated that by 2050, under the arable land protection and natural development scenarios, CS will decrease by 0.07% and 0.005%, respectively, encroachment on undeveloped mountain areas, while the ecological protection scenario gives priority to protecting the carbon sinks of woodland and grassland, and CS will increase by 0.37%. This study supports the implementation of targeted ecological protection measures through topographic gradient zoning, provides a reference for policy makers in similar topographic regions to effectively manage the spatial heterogeneity of CS, and helps further strengthen global and regional climate change mitigation efforts.

Spatiotemporal dynamics and driving factors of energy-related carbon emissions in the Yangtze River Delta region based on nighttime light data

Owing to China’s massive area and vastly differing regional variations in the types and efficiency of energy, the spatiotemporal distributions of regional carbon emissions (CE) vary widely. Regional CE study is becoming more crucial for determining the future course of sustainable development worldwide. In this work, two types of nighttime light data were integrated to expand the study’s temporal coverage. On this basis, the distribution of energy-related CE in the Yangtze River Delta (YRD) region of China was estimated at a multispatial scale. Then the spatiotemporal dynamics of CE were explored based on the estimated results. The findings showed that the growth rate of CE in the YRD displayed three stages, and the total CE fluctuated upward. The spatial pattern of CE demonstrated a step-like decline from east to west. However, the Gini coefficient indicated that the differences in CE between cities gradually decreased since the CE had a strong spatial positive correlation in the YRD. Multiple factors affected the spatial variation of CE in the YRD, with economic level and population as the “critical” influencing elements, which determined the absolute amount of CE. This study provides a long-term analysis of CE dynamics while enhancing explanatory accuracy. The methods offer novel perspectives and tools for regional CE research. The results reveal spatial heterogeneity and clustering patterns of CE within regions, contributing valuable scientific evidence for monitoring regional CE and formulating emission reduction policies.

Spatial Temporal Differences in Carbon Emissions from Land-Use Change and Carbon Compensation in Gansu Province, China

This study employs land use and energy consumption data from Gansu Province spanning the years 2005 to 2020 and utilizes models to estimate carbon emissions and the corresponding carbon compensation values. The research calculated the carbon emissions and compensation for various administrative regions over different time periods, revealing the following insights: (1) There is a notable increasing trend in net carbon emissions due to land-use changes in Gansu Province, characterized by an initial swift rise, followed by a more gradual growth pattern. Construction land is identified as the primary contributor to carbon emissions, increasing from 26 million tons in 2005 to 55.3 million tons in 2020, which is an increase of 1.80 times; meanwhile, forested areas, as significant carbon sinks, show a slight increase in carbon absorption from 2.33 million tons in 2005 to 2.35 million tons in 2020. (2) The municipalities with high net carbon emissions are predominantly Lanzhou, Qingyang City, and Jiuquan City, which are marked by high levels of carbon emissions and low compensation rates. In contrast, regions with lower net carbon emissions are mainly found in the Gannan Tibetan Autonomous Prefecture, where emissions are minimal and compensation rates are relatively high. A similarity in the spatial distribution patterns of both net carbon emission intensity and total net carbon emissions is observed among these cities. Alterations in land use have a significant impact on regional carbon emissions. Investigating the spatiotemporal variations of land-use change and carbon compensation in Gansu Province is essential for comprehending the dynamics of regional carbon emissions, developing effective emission reduction strategies, and fostering low-carbon development.

A study on the carbon emission reduction pathways of China’s digital economy from multiple perspectives

As the share of the digital economy’s output continues to rise each year, the emergence of new industries such as e-commerce, mobile payments, and cloud computing has opened new avenues for carbon emission reduction (CER). Based on panel data from 30 provinces in China, this article systematically analyzes the CER pathways of China’s digital economy (DE) from the perspectives of direct effects, indirect effects, threshold effects, and heterogeneity analysis. The main conclusions are as follows: (1) China’s DE has a significant CER effect. (2) The DE can indirectly reduce regional carbon emissions (CE) by industrial structures and technological innovation, with the mediating effect of technological innovation being more significant than that of industrial structure. (3) Urbanization has threshold effects on the CER effect of China’s DE. Under the influence of urbanization, there is an inverted U-shaped relationship between DE and CE. (4) Heterogeneity analysis finds that, compared to other types of provinces, the CER effect of DE is stronger in non-resource-based and economically developed provinces. (5) We propose five tailored recommendations for CER: fostering the synergistic development of the DE and industrial structure, strengthening the role of technological innovation, advancing urbanization and carbon reduction in a differentiated manner, formulating distinct policies for resource-based and non-resource-based provinces, and enhancing the construction of digital infrastructure in less-developed regions. This article not only establishes a more comprehensive connection between the DE and CER, but also reveals the differences in the role of technological innovation, industrial structure optimization, urbanization and other factors in the carbon reduction effect of the DE through the comparison of different paths and mechanisms.

Regional Carbon Storage Dynamics Driven by Tea Plantation Expansion: Insights from Meitan County, China

Over the past two decades, the tea plantation area in Meitan County, China, has expanded nearly 30-fold, driving significant land use and cover changes (LUCC) with unclear impacts on regional carbon storage. This study uses the PLUS-InVEST model to analyze LUCC impacts on carbon storage from 2000 to 2020 and predict future changes by 2060. Results show a decline in total carbon storage from 3977.83 × 104 t in 2000 to 3960.85 × 104 t in 2020, primarily due to reductions in cultivated land and grassland. Although carbon storage in forest land and tea plantations increased, the overall trend remained negative. Multi-scenario simulations indicate that the sustainable development scenario (SDS) mitigates carbon loss, with a decrease of 31.53 × 104 t, compared to larger reductions in the natural development (NDS) and economic development (EDS) scenarios. Under the SDS scenario, carbon storage in forest land and grassland increased while construction land expansion was controlled. This study emphasizes optimizing land use and agricultural management to enhance carbon sequestration and protect ecosystems, highlighting the need for a balance between ecological protection and economic development for sustainable carbon management in Meitan County.

Study on the Impact Mechanism of Wenzhou's Digital Economy Development on Carbon Emissions from the Perspective of Resilience

With the proposed dual-carbon target and the continuous development of digital economy, the impact of digital economy on regional carbon emissions has been paid more and more attention. According to the analysis of the development data of Wenzhou city digital economy, the results show that the development of digital economy is helpful to reduce Wenzhou carbon emissions, and the resilience of industrial chain and urban resilience are important mechanisms for digital economy to promote carbon emission reduction. Based on this, suggestions are put forward to speed up infrastructure construction and implement differentiated dynamic development strategy according to the characteristics of Wenzhou city.

Regional carbon emission efficiency evaluation combining gray forecasting and game cross-efficiency analysis: The case of Yangtze River Delta

Carbon emission efficiency (CEE) reflects the interplay between carbon emissions and the economy, which refers to achieving more economic benefits and lower carbon emissions while considering energy, labor, and capital inputs. Assessing regional CEE is crucial for evaluating the level of China's regional low-carbon economic development. Thus, this paper proposes a scenario-based hybrid model with a foresight perspective and game cross-efficiency (GCE) analysis. It measures the future CEE of 41 Yangtze River Delta (YRD) cities from 2023 to 2030. The improved gray forecasting models generate the input and output datasets for GCE analysis, and the assurance region constraint simulates the carbon emission and energy consumption dual-control policy. The results show that: (1) the CEEs of 41 cities are generally low, with an average of 0.2142. Shanghai has the highest CEE, 0.8089, while Tongling has the lowest, 0.0307, under the current policy constraint. (2) Under the four control policy scenarios, the CEE of the YRD urban agglomeration generally follows a U-shaped trend. It indicates that the dual-control policy may lead to a short-term decline in the CEE of YRD, but in the long term, it may gradually increase in 2025 or 2026. (3) Spatial–temporal analysis reveals that the government should flexibly optimize and update the carbon intensity constraint value based on regional development differences and focus more on energy consumption. These results provide forward-looking guidance for China's regional low-carbon and high-quality development.

Sub-District Level Spatiotemporal Changes of Carbon Storage and Driving Factor Analysis: A Case Study in Beijing

Analyzing the current trends and causes of carbon storage changes and accurately predicting future land use and carbon storage changes under different climate scenarios is crucial for regional land use decision-making and carbon management. This study focuses on Beijing as its study area and introduces a framework that combines the Markov model, the Patch-based Land Use Simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to assess carbon storage at the sub-district level. This framework allows for a systematic analysis of land use and carbon storage spatiotemporal evolution in Beijing from 2000 to 2020, including the influence of driving factors on carbon storage. Moreover, it enables the simulation and prediction of land use and carbon storage changes in Beijing from 2025 to 2040 under various scenarios. The results show the following: (1) From 2000 to 2020, the overall land use change in Beijing showed a trend of “Significant decrease in cropland area; Forest increase gradually; Shrub and grassland area increase first and then decrease; Decrease and then increase in water; Impervious expands in a large scale”. (2) From 2000 to 2020, the carbon storage in Beijing showed a “decrease-increase” fluctuation, with an overall decrease of 1.3 Tg. In future carbon storage prediction, the ecological protection scenario will contribute to achieving the goals of carbon peak and carbon neutrality. (3) Among the various driving factors, slope has the strongest impact on the overall carbon storage in Beijing, followed by Human Activity Intensity (HAI) and Nighttime Light Data (NTL). In the analysis of carbon storage in the built-up areas, it was found that HAI and DEM (Digital Elevation Model) have the strongest effect, followed by NTL and Fractional Vegetation Cover (FVC). The findings from this study offer valuable insights for the sustainable advancement of ecological conservation and urban development in Beijing.