Carbon Climate Research Articles

Climate change disclosure and carbon performance of Chinese listed companies: exploring the moderating effects of climate governance and corporate environmental governance

Climate change poses numerous risks to businesses, leading to growing attention from governments and stakeholders toward corporate climate change disclosures. However, whether these disclosures can effectively drive companies to enhance their carbon reduction efforts remains an urgent question. Using panel data from heavily polluting companies in China, this study employs generalized structural equation modeling (GSEM) to empirically examine the moderating effects of government-level climate governance and corporate-level environmental governance on the relationship between climate change disclosure and carbon performance. The results indicate that the interaction between climate governance and climate change disclosure significantly promotes improvements in carbon performance, whereas the impact of corporate environmental governance is comparatively limited. These findings underscore the critical role of government-driven climate governance in enhancing the effectiveness of climate change disclosures and provide practical recommendations for policymakers and corporations to improve climate disclosure practices and advance carbon reduction efforts.

Global environmental sustainability: the role of economic, social, governance (ECON-SG) factors, climate policy uncertainty (EPU) and carbon emissions

Global environmental sustainability: the role of economic, social, governance (ECON-SG) factors, climate policy uncertainty (EPU) and carbon emissions

Rapid pyrolysis of paleo-oil pools recorded by fluid inclusions in an Ediacaran carbonate reservoir, southern China: Implications for the end-Guadalupian mass extinction

Thermogenic greenhouse gas emission associated with large igneous province emplacement has previously been hypothesized as a possible contributor to global warming that triggered mass extinctions in Earth’s history, yet this issue remains inadequately understood due to a lack of robust evidence. We present a well-preserved mineralogical, fluid inclusion, and geochemical record indicating a rapid pyrolysis of vast paleo-oil pools in an Ediacaran carbonate reservoir in the central Sichuan Basin (southern China) relating to the eruption of the Emeishan large igneous province that potentially contributed to the end-Guadalupian extinction (ca. 259 Ma). We conclude that the pyrolysis of the paleo-oil pools occurred at ca. 258.4 ± 7.3 Ma associated with the emplacement of the Emeishan large igneous province, causing a rapid increase of temperature and pressure with an estimated transient reservoir pressure of nearly twice that of the lithostatic pressure. A subsequent drop in the formation pressure occurred as the temperature continued to rise, indicating that massive over-pressured methane in the reservoir had been rapidly emitted into the atmosphere along faults or magmatic pipes, ultimately contributing to the end-Guadalupian extinction. Our study highlights the significance of massive emission of thermogenic methane related to large igneous provinces for climate change, carbon cycling, and biological evolution in Earth’s history.

Drivers and Challenges in Achieving Corporate Carbon Neutrality—Qualitative Investigation of Carbon Capture, Utilization, and Storage Technologies

ABSTRACTThe importance of attaining carbon neutrality (CN) has progressively increased in addressing climate change, and consequently, carbon capture, utilization, and storage (CCUS) has emerged as a promising tool in this pursuit. Nevertheless, a dearth of scholarly discourse exists about the factors that drive the implementation of CCUS in the context of corporate CN and the challenges that inhibit the adoption of CCUS for CN. This study aims to bridge this knowledge gap by analyzing open‐ended essays to gain cardinal insights into the experiences and perspectives of companies successfully implementing CCUS for CN. The collected (qualitative) data underwent rigorous analysis to synthesize three distinct dimensions encompassing various aspects of CCUS to achieve CN. The study identifies multiple CCUS components employed to manage carbon emissions effectively. It underscores the five drivers accelerating the adoption of CCUS and classifies them as intrinsic or extrinsic drivers. Various challenges impeding the successful adoption of CCUS have been pinpointed as emanating from financial, human resource, operational, technological, and strategic challenges; technology development and infrastructural challenges, environmental impacts; and social and regulatory challenges. Our research renders valuable theoretical and practical insights based on the prevalent discourse regarding the implementation of CCUS in the field of CN with a comprehensive and inclusive approach.

Long-term (2000–2020) global 0.05° continuous atmospheric carbon dioxide mapping combining OCO-2 observations and model simulations

We reconstructed a global continuous 8-day XCO2 (column-averaged CO2 dry air mole fraction) product (GCXCO2) at a spatial resolution of 0.05° from 2000 to 2020, combining terrestrial/marine remote sensing data and model simulations based on developed and tested stacking machine learning method. The GCXCO2 product has the similar spatial pattern with OCO-2 satellite observations but with global seamless coverage, showing a higher spatial resolution and accuracy than CarbonTracker and CAMS model simulation data. A novel dynamic normalization strategy was developed to handle the great temporal variation issue and ensure the temporal expansion of the prediction model. The sampled based 10-fold cross-validation shows an overall satisfactory result at a global scale, with R2 = 0.974 and root-mean-square error (RMSE) = 0.551 ppm (parts per million). Further spatial extension and temporal prediction experiments also proved that dependable results could be obtained in the regions and time periods without valid OCO-2 satellite observations (R2 = 0.958 and R2 = 0.886, respectively). Compared with Total Carbon Column Observing Network (TCCON) ground station observations, the GCXCO2 demonstrates a better accuracy and a higher spatial resolution than the model simulation data. Based on the GCXCO2 product, an upward annual trend of approximately 2.105 ppm/year can be found for global XCO2 between 2000 and 2020, with greater seasonal fluctuations in the Northern Hemisphere than in the Southern Hemisphere. This product is one of some remote sensing-based global high-precision long-term XCO2 datasets and an important tool to help advance the understanding of climate change and carbon balance, but also to detect CO2 concentration anomalies. The dataset can be obtained publicly at doi:https://doi.org/10.5281/zenodo.10083102 (Guan and Sun, 2023).

Integrating Traditional Knowledge and Modern Technologies for Renewable Energy Adoption in Sub-Saharan Africa: Advancing Climate Resilience and Carbon Reduction Strategies

This paper explores the potential of reducing carbon footprints and greenhouse gas emissions in climate-sensitive regions of Sub-Saharan Africa by integrating traditional knowledge with modern renewable energy technologies. Drawing on a mixed-methods approach that combines quantitative energy data and qualitative insights from expert interviews and policy reviews, the study analyzes the implementation of renewable energy sources such as solar, wind, and hydro. Data from global organizations, including the International Energy Agency (IEA) and the World Bank, supports the investigation. The findings highlight renewable energy's transformative potential for emissions reduction, energy security, and economic growth, with solar energy demonstrating exceptional promise for rural electrification. Despite its benefits, adoption is hindered by financial constraints, inadequate infrastructure, and regulatory challenges. The study underscores the need for climate resilience strategies such as energy storage integration and grid upgrades to support reliable access to renewable energy. By linking renewable energy with sustainability and resilience theories, the research emphasizes the role of adaptive infrastructure in fostering economic development and environmental health. Key recommendations include improving financing mechanisms, enacting supportive policy frameworks, strengthening regional partnerships, and prioritizing energy storage and grid modernization. This study provides actionable insights for policymakers, energy stakeholders, and development organizations, emphasizing that overcoming barriers to renewable energy adoption is critical for achieving sustainable energy access, reducing emissions, and aligning with global climate goals.

Understanding ecosystem services of detailed forest and wetland types using remote sensing and deep learning techniques in Northern China

Spanning both temperate and sub-frigid zones, Northeast China boasts typical boreal forests and abundant wetland resources. Because of these attributes, the region is critically significant for global climate regulation, carbon sequestration, and biodiversity preservation. While existing research explores the ecosystem service (ESs) functions of different land cover types, a thoroughly in-depth investigation into the ESs of detailed forest and wetland types is essential. This study addresses this deficiency by combining remote sensing and deep learning techniques, employing a lightweight convolutional neural network (CNN) model and a decision tree for the large-scale classification of forests and wetlands. The ESs of various forest and wetland types—encompassing habitat quality, carbon stock, and soil retention—were assessed during two periods (2008 and 2018) in Heilongjiang Province. Key factors determinants of ESs were identified using the Geodetector tool. The results indicated an overall accuracy of 0.77 in 2008 and 0.78 in 2018 for forest type classification, and 0.88 in 2008 and 0.87 in 2018 for wetland type classification. In particular, the transition from mixed broadleaf forests to mixed coniferous-broadleaf forests dominated changes from 2008 to 2018, probably due to natural succession. Among forest types, Mongolian oak forests exhibited the highest carbon stock and soil retention capacity owing to their rapid growth and deep root systems. Mixed broadleaf forests exhibited superior habitat quality, suggesting minimal disturbance. Habitat quality, carbon stock, and soil retention were found to be significantly influenced by human activity, atmospheric quality, and topographic factors, respectively. By leveraging remote sensing and deep learning methodologies, this study offers a comprehensive analysis of forests and wetlands, elucidating the nuanced ecosystem roles of specific forest and wetland types.

Variable emission factors of CH4 and N2O from WWTPs: A model-based analysis on available data

Climate change and carbon emissions are increasingly becoming a global concern, and thus wastewater treatment plants (WWTPs) are also receiving extensive attention due to direct greenhouse gas (GHG) emissions of methane (CH4) and nitrous oxide (N2O). Although there have been many emission factors (EFs) of CH4 and N2O in literature, they are changeful due to different processes and boundaries, which limits their values for reference and comparison. With this study, in situ monitored CH4 and N2O data reported in literature were retrieved for recalculating their EFs. The average EFs are found to be 0.0011 g CH4/g BOD5-influent, and 0.0017 g N2O-N/g TNinfluent, based on the secondary treatment. Subsequently, the data were analyzed using multivariate linear regression and neural network. The results indicate that BOD5 is the first factor affecting the EF of CH4, revealing a negative correlation and that TN is the second factor affecting the EF of CH4, but having a positive correlation. On the other hand, the neural network is a powerfully predictive and generalizable tool for EFN2O. BOD5 is negatively correlated with EFN2O, and EFN2O reaches to its maximum value at TN = 35 mg/L. Overall, the direct GHG emission intensity is the lowest in the AAO and AO processes, or with the BOD5/TN ratio between 2.5 and 4.9. Medium-sized WWTPs and the Oceania region exhibit the highest GHG emission intensity. With this study, an approximate approach is established to estimate the EFs of CH4 and N2O, which can facilitate to account the carbon footprint of WWTPs and also to aid in optimizing their operational schemes.

Climate policy and carbon leakage: Evidence from the low-carbon city pilot program in China

The low-carbon city pilot (LCCP) policy, aimed at promoting the transformation of cities towards low-carbon practices, is an important action for China to mitigate climate change. Though this climate policy brings positive effects in many aspects, the issue of potential carbon leakage due to regional disparity has been overlooked. This paper introduces a novel approach by employing the spatial DID model and prefecture-level city data to assess the LCCP policy and its implications for carbon leakage. The innovative aspect lies in its spatially differentiated analysis, which reveals that while the LCCP policy effectively reduces carbon emissions of local pilot cities, it simultaneously triggers carbon leakage to neighboring non-pilot cities, with the leakage effects diminishing with increasing distance. Additionally, this study uncovers that carbon leakage risks are mitigated when multiple pilot cities are geographically clustered, enhancing the overall effectiveness of the policy. Finally, the impact channel analysis uniquely identifies that carbon leakage results from the inter-city relocation of high-carbon industries, such as manufacturing, which exhibits spatial attenuation and temporal lag effects. This study advances the understanding of low-carbon city pilot programs and provides valuable insights for mitigating carbon leakage risks.

Time persistence of climate and carbon flux networks

The persistence of the global climate system is critical for assuring the sustainability of the natural ecosystem. However, persistence at a network level has been rarely discussed. Here we develop a framework to analyze the time persistence of the yearly networks of climate and carbon flux, based on cross-correlations between sites, using daily data from China, the contiguous United States, and the Europe land region. Our framework for determining the persistence is based on analyzing the similarity between the network structures in different years. Our results reveal that the similarity of climate and carbon flux networks in different years are within the range of 0.57 ± 0.07, implying that the climate and carbon flux in the Earth’s climate system are generally persistent and in a steady state. We find a very small decay in similarity when the gap between years increases. Moreover, we find that the persistence of climate variables and carbon flux in the three regions decreases when considering only long range links. Analyzing the persistence and evolution of the climate and carbon flux networks, enhance our understanding of the spatial and temporal evolution of the global climate system.

Contributions of ecological restoration policies to China’s land carbon balance

Unleashing the land sector’s potential for climate mitigation requires purpose-driven changes in land management. However, contributions of past management changes to the current global and regional carbon cycles remain unclear. Here, we use vegetation modelling to reveal how a portfolio of ecological restoration policies has impacted China’s terrestrial carbon balance through developing counterfactual ‘no-policy’ scenarios. Pursuing conventional policies and assuming no changes in climate or atmospheric carbon dioxide (CO2) since 1980 would have led China’s land sector to be a carbon source of 0.11 Pg C yr−1 for 2001–2020, in stark contrast to a sink of 175.9 Tg C yr−1 in reality. About 72.7% of this difference can be attributed to land management changes, including afforestation and reforestation (49.0%), reduced wood extraction (21.8%), fire prevention and suppression (1.6%) and grassland grazing exclusion (0.3%). The remaining 27.3% come from changes in atmospheric CO2 (42.2%) and climate (−14.9%). Our results underscore the potential of active land management in achieving ‘carbon-neutrality’ in China.

Assessment and Dynamic Prediction of Green Space Ecological Service Value in Guangzhou City, China

As an important part of the urban ecosystem, urban green space provides a variety of ecosystem services, including climate regulation, soil conservation, carbon sink and oxygen release, and biodiversity protection. However, existing remote sensing evaluation methods for ecological service value lack the evaluation indicators of ecosystem service value for Guangzhou, China, and the evaluation method depends on the land cover type. Based on remote sensing technology and random forest algorithm, this study addresses these gaps by integrating remote sensing technology with a random forest algorithm to enhance the accuracy and rationality of ESV assessments. Focusing on Guangzhou, China, we improved the ecological service value evaluation system and conducted dynamic predictions based on land-use change scenarios. Our results indicate that the total ESV of Guangzhou’s green space was USD 7.323 billion in 2020, with a projected decline to USD 6.496 billion by 2030, representing a 12.37% reduction due to urbanization-driven land-use changes. This research highlights the noticeable role of green spaces in urban sustainability and provides robust, data-driven insights for policymakers to design more effective green space protection and management strategies. The improved assessment framework offers a novel approach for accurately quantifying urban ecosystem services and predicting future trends.

Progress in Corrosion Protection Research for Supercritical CO2 Transportation Pipelines

Carbon Capture, Utilization, and Storage (CCUS) technology is an emergent field with the potential for substantial CO2 emissions reduction, enabling low-carbon utilization of fossil fuels. It is widely regarded as a critical technology for combating global climate change and controlling greenhouse gas emissions. According to recent studies, China has identified CCUS as a key emissions reduction technology in climate change response and carbon neutrality objectives. Within this framework, supercritical CO2 (SC-CO2) transport pipelines are an essential means for efficient and safe transportation of CO2. Corrosion protection of pipelines enhances the efficiency and safety of CCUS technology and supports broader implementation and application. This paper reviews the current research on corrosion protection for SC-CO2 transport pipelines, discusses effect factors, compares various corrosion protection strategies, and analyzes the challenges in corrosion protection of SC-CO2 transport pipelines. It concludes with a perspective on future research and development directions in this field. This paper is dedicated to providing new research strategies for pipeline corrosion protection in CCUS technology in the future, and providing technical support for pipeline corrosion protection in CCUS industrial applications.

Evaluating tree-ring proxies for representing the ecosystem productivity in India.

Terrestrial ecosystems are one of the major sinks of atmospheric CO2 and play a key role in climate change mitigation. Forest ecosystems offset nearly 25% of the global annual CO2 emissions, and a large part of this is stored in the aboveground woody biomass. Several studies have focused on understanding the carbon sequestration processes in forest ecosystems and their response to climate change using the eddy covariance (EC) technique and remotely sensed vegetation indices. However, very few of them address the linkage of tree-ring growth with the ecosystem-atmosphere carbon exchange, and nearly none have tested this linkage over a long-term (> 100 years) - limited by the short-term (< 50 years) availability of measured ecosystem carbon flux. Nevertheless, tree-ring indices can potentially act as proxies for ecosystem productivity. We utilise the Coupled Climate Carbon Cycle Model Intercomparison Project (C4MIP) model outputs for its 140-year-long simulated records of mean monthly gross primary productivity (GPP) and compare them with the tree-ring growth indices over the northwestern Himalayan region in India. In this study, we examine three coniferous tree species: Pinus roxburghii and Picea smithiana wall. Boiss and Cedrus deodara and find that the strength of the correlation between GPP and tree ring growth indices (RWI) varies among the species.

Climate board governance and carbon assurance – European evidence

Purpose This paper aims to investigate the effect of climate board governance based on (1) carbon change committees, (2) critical mass of female board members and (3) carbon-related executive compensation, on the voluntary implementation and quality of carbon assurance. Design/methodology/approach Using stakeholder and critical mass theories, 978 firm-year observations for European Union firms for the 2017–2021 period are collected. Climate board governance and carbon assurance proxies come from the Carbon Disclosure Project. Findings Correlation and logit regression analyses show that climate board governance significantly improves carbon assurance (quality). The findings are robust to various robustness and endogeneity checks and are of great importance for researchers, regulators and business practice. Originality/value This analysis mainly contributes to previous studies by using a combined sustainable board governance score as a major driver of corporate carbon assurance practices for the first time.

Municipal Waste Treatment: Climate Change Mitigation Impacts and Carbon Credit Market Effects

Objective: Analyze the feasibility of proposing a comprehensive methodology for inventorying greenhouse gas (GHG) emissions from urban waste, with the goal of enhancing the attractiveness of carbon credits and demonstrating their impacts on climate mitigation. Theoretical Framework: The research is grounded in theories related to climate change, the circular economy, and urban waste management. Global warming is discussed through the works of authors such as David Wallace-Wells and José Goldemberg, alongside concepts from the GHG Protocol and Life Cycle Assessment (LCA). Method: The methodology involves a bibliographic review, document analysis, and exploratory research using secondary and qualitative data. Scientific articles and documents from organizations such as the IPCC and EMBRAPA, among other academic sources, were utilized. Results and Discussion: The research emphasizes that proper management of urban waste, aligned with the principles of the circular economy, can substantially reduce GHG emissions. "Zero Waste" projects are analyzed as viable alternatives for mitigating emissions and obtaining carbon credits. The Life Cycle Assessment (LCA) of products is identified as crucial for inventorying emissions across various stages of the life cycle, thereby facilitating the certification process for carbon credits. Research Implications: The proposed methodology can encourage new investments in recycling and waste reuse, as well as support the establishment of waste treatment units that maximize resource recovery and minimize environmental impacts.Originality: This study contributes to the literature by presenting an innovative approach to the management of urban waste and carbon credits, highlighting the importance of LCA in certifying reduced emissions. Originality: This study contributes to the existing literature by introducing an innovative approach to urban waste management and carbon credits, underscoring the importance of LCA in the certification of reduced emissions.

Evaluating the performance of carbon dioxide and methane observations from carbon-monitoring satellite products over China

As the primary greenhouse gases contributing to climate change, carbon dioxide (CO₂) and methane (CH4) play a critical role in the Earth's radiative balance and temperature regulation. Continuous and accurate satellite monitoring of CO₂ and CH₄ is essential for developing effective policies and strategies to mitigate their impacts. This study utilizes ground-based Fourier-transform infrared measurements from the Total Carbon Column Observing Network (TCCON) to evaluate the performance of current mainstream carbon-monitoring satellites over China, the world's largest carbon emitter. Our findings show that TanSat performs excellently for CO₂ observations at both Hefei and Xianghe sites, achieving a standard deviation (SD) of about 2 ppm and a maximum bias of 0.25 ppm, dropping to 0.02 ppm at Xianghe. In contrast, Orbiting Carbon Observatory (OCO)-3 and Greenhouse Gases Observing Satellite (GOSAT)-2 CO2 measurements are slightly less reliable. For CH₄ monitoring, GOSAT outperforms GOSAT-2, with a SD of around 14 ppb and bias within 2 ppb, compared to GOSAT-2's 17.58 ppb SD and 2.15 ppb bias at Hefei. These results indicate that the latest carbon-monitoring satellites are less precise and accurate than their predecessors. Additionally, we provide detailed assessments of the data products based on different spatial matching criteria. We also discuss the variation in satellite accuracy over time, revealing periodic bias variations and the instability in satellite performance. Furthermore, while the spatial distribution trends of satellite acquisitions are generally consistent on an annual scale, we observe non-negligible differences in the annual averages across specific land surfaces. Our study presents a meticulous evaluation of the reliability of satellite-based carbon-monitoring products over the Chinese region and provides scientific evidence for analyzing uncertainty in carbon source-sink studies.

Localized environmental governance for carbon dioxide removal in small island developing states: Challenges and opportunities

Accelerating the net-zero economy requires understanding energy consumption patterns, renewable technology attitudes, and acceptance of sustainable energy systems. This study explores how behavioral modification campaigns reduce residential energy use, promote energy-efficient products, and improve consumption patterns. Decentralization and digitization of energy infrastructure have spawned new markets and business models, giving customers more energy control and improving efficiency. The unique regulatory structure of Ireland's single power market makes it a compelling case study. The study examines Irish consumers' sustainable energy beliefs and behaviors from 1992 to 2022 using a 40-question survey. The data show that smart energy solutions for carbon-conscious families have pros and cons. Consumer knowledge of environmental issues and willingness to accept new energy technology are high, but cost and perceived complexity remain impediments to wider implementation. The research emphasizes technical innovation and behavioral change in energy reduction and greener energy promotion. These findings can help engineers, industry stakeholders, and politicians reach climate policy goals and carbon neutrality. Policymakers should support smart energy adoption, reduce cost barriers, and simplify the process. A fair and sustainable net-zero economy transition requires consumer education and decentralized energy market policies. This research underscores the importance of customized policies that leverage technology and behavior to mitigate the effects of climate change.

Unveiling mid-century conservation priorities: Co-occurrence of biodiversity, climate change exposure, and carbon storage in the Middle and Lower Yangtze River Basin, China

The global challenges of biodiversity loss and climate change necessitate the implementation of integrated conservation strategies. A forward-looking framework is necessary to reconcile climate change adaptation and mitigation efforts with biodiversity goals, supporting effective long-term planning and management of protected areas (PAs) network. This study develops a comprehensive approach to identify priority areas for biodiversity conservation until 2050, integrating assessments of species distributions, climate change exposure and carbon storage under distinct shared socioeconomic pathways. The Middle and Lower Yangtze River Basin (MLYRB) in China serves as a case study. Using an ensemble species distribution modeling (ESDM) method, we predicted the distributions of 435 threatened terrestrial vertebrate species and plants. We then mapped their richness in the MLYRB. Distance-based climate velocity analysis was performed to identify climate change coldspots and hotspots. The patch-generating land use simulation (PLUS) model and InVEST model were coupled to simulate carbon storage changes. Systematic conservation planning tool, Zonation, was used to prioritize species conservation hotspots. By examining the co-occurrence of these hotspots with climate change coldspots/hotspots and areas of high carbon storage, conservation priorities for the MLYRB were revealed. Our findings indicate that 18.27–24.94 % of the MLYRB risks over 20 % species richness declines by 2050 under various scenarios, while increases over 10 % are projected for only 0.81–1.75 % of the area. Co-occurrence analysis highlights significant associations, such as a 32.26 % overlap between species conservation hotspots and climate change hotspots in SSP1–2.6. Particularly noteworthy is the substantial co-occurrence (57.93–59.50 % across scenarios) between areas maximizing species conservation and carbon storage. The identified conservation priority areas, covering 41.95 % of the MLYRB (441554 km2), hold potential for long-term species conservation, climate resilience, and nature-based climate solutions by 2050. However, only 6.08 % of these priorities currently benefit from protection. These results offer valuable guidance for region-specific landscape management and conservation policy aligned with international goals. The presented methodology provides a broader application, serving as a valuable resource for prioritizing conservation efforts in other regions integrating biodiversity, climate change adaptation, and mitigation goals.

How do e-government and green technology innovation affect carbon emissions: Evidence from resource-rich countries in the Shanghai Cooperation Organization

The global challenges related to climate change and carbon emissions are constantly escalating. It is important to explore the factors that affect carbon emissions in countries rich in natural resources. However, the nexus among natural resources, e-government, green technology innovation, and carbon emissions has not yet been demonstrated. Therefore, this study explores the impact of the above three dependent variables on carbon emissions using data from five resource-rich SCO countries spanning from 2000 to 2022. This study further controls the quality of the institution and GDP. Unlike existing studies, it introduces an innovative interaction term between e-government and green technology innovation. The cross-sectional enhanced distributed hysteresis (CS-ARDL) method was employed for the analysis. The findings indicate that natural resources and GDP positively contribute to carbon emissions, while institutional quality and the interaction between green technology innovation and e-government have a negative impact. In the short run, green technology innovation has a non-significant impact on carbon emissions, while e-government has a positive impact. However, in the long run, both green technology innovation and e-government have a negative impact on carbon emissions. These findings suggest that promoting the simultaneous development of e-government and green technology innovation can help reduce carbon emissions. The paper is of great significance for further promoting the construction of e-government. In addition, the paper is of great reference value for policymakers in launching carbon reduction policies.