Global Carbon Emission Reduction Research Articles

Combined multi-level context aggregation and attention mechanism method for photovoltaic panel extraction from high resolution remote sensing images

ABSTRACT In the context of global carbon emission reduction, solar photovoltaic (PV) technology is experiencing rapid development. Using high-resolution remote sensing images to accurately obtain PV information over a large region, including location and size, has the advantages of high statistical efficiency and timely data update for the PV energy management. Due to the intra-class diversity of PV panels and the intricate variability in their deployment environments, existing semantic segmentation methods often have problems such as under-segmentation and mis-segmentation. To alleviate these problems, this paper proposes an improved DeepLabv3+ semantic segmentation network to more accurately extract PV panels from high-resolution remote sensing images. With the aim of alleviating under-segmentation, a multi-level context aggregation module is developed. This module can enhance the model’s ability to learn the characteristics of PV panels and their surrounding environment by aggregating rich contextual information from multi-scale and semantic levels. To alleviate the problem of mis-segmentation, a hybrid attention module is introduced. This module sequentially and adaptively adjusts the weight distribution in both the channel and spatial dimensions, thus enabling the model to focus more on the feature information and spatial positions of PV objects. Experiments conducted on a self-constructed Beijing PV segmentation dataset show that the method in this paper has advantages of completeness and accuracy in extracting PV panels compared to the baseline model and current mainstream semantic segmentation network. In addition, the results of experiments on extracting PV panels in real region show that our model also has good stability and generalization capability.

Strategic Analysis of International Oil and Gas Companies’ Response to Global Carbon Emission Reduction Initiatives

This study investigates the role of oil and gas companies, particularly INDOPERTA, in mitigating global carbon emissions among rising energy demands and a shift toward coal and oil. This study highlights the urgency for strategic energy realignment and cleaner, sustainable sources, drawing from International Energy Agency (IEA) reports and various studies. The objective is to understand how companies such as INDOPERTA can integrate operations with effective carbon emission reduction strategies. A mixed-methods approach is employed, combining qualitative analysis of stakeholder perspectives with the AHP to prioritize initiatives based on identified criteria. Findings reveal compliance and regulation and leadership as critical areas needing focused resource allocation. The AHP analysis underscores the strategic importance of renewable energy investment and the pivotal role of leadership and innovation. It suggests a multifaceted approach involving regulatory adherence, international cooperation, and leadership development for effective carbon reduction. The study concludes that international oil and gas companies are at a crucial juncture. They must align their operations with global environmental goals and innovate to remain relevant in the climate agenda. It provides a roadmap for strategy realignment toward sustainability. However, this study has limitations, including its geographical focus, the dynamic nature of the industry, subjectivity in qualitative research and AHP, and reliance on secondary data. These findings highlight the need for ongoing research that adapts to industry changes and global carbon reduction efforts, ensuring that strategies remain relevant and effective.

Digitalization in prefabricated constructions and reducing carbon emission: empirical evidence from China.

Taking the implementation of digitalization in prefabricated constructions (DPC) as an exogenous shock, this study utilizes provincial panel data of China from 1997 to 2019, and uses the difference-in-differences model to identify the causal effect of DPC on carbon emissions in the construction industry (CECI). This study shows that DPC effectively reduces CECI with the results withstanding rigorous testing, including parallel trend tests, placebo tests, and a heterogeneous time-varying treatment test based on Bacon decomposition and the DIDm approach. The mechanism test confirms that DPC reduces CECI through two channels, which are improving existing building technologies and promoting better management capabilities. At the same time, further analyses show that the carbon emissions reduction effect of DPC is more pronounced in regions with a higher degree of marketization, financial development, human capital and environmental regulation. This study contributes to understanding the underlying mechanisms between DPC and CECI, providing new insights for the deeper integration of digital technologies and the construction industry. It offers new avenues for revitalizing the construction sector and facilitating achieving global carbon emissions reduction goals.

How do industrial land transfer modes impact carbon emissions? An intermediation perspective based on industrial structure.

Industrial land is currently the main carrier and important source of global carbon emissions, and as the world's largest developing country, China's large-scale and diversified industrial land supply has made it the world's largest carbon emitter. Therefore, researching the impact of different supply methods of industrial land on carbon emissions and its impact paths in China can help provide a reference for other countries to reduce carbon emissions from the perspective of urban industrial land management, which is of great significance for effectively promoting global carbon reduction. Based on this, this paper analyses the impact of different supply methods of industrial land on carbon emissions and its urban heterogeneity using the SYS-GMM and chain-mediated effects models for 285 cities in China from 2008 to 2020. The study found that, in general, the impact of different industrial land transfer modes on carbon emission has hysteresis and persistence. Agreement and listing transfer with government intervention can significantly exacerbate carbon emissions, while more market-based bidding and auction transfer can dampen carbon emissions. In terms of intermediary effects, the transfer of industrial land by agreement and listing will inhibit the rationalization and advancement of industrial structure, thus aggravating carbon emissions. The transfer of industrial land by bidding and auction will create barriers to entry and a crowding-out effect, promote the rationalization of industrial structure and the transformation and upgrading of industrial structure and moderate carbon emissions. In terms of city heterogeneity, there is urban heterogeneity in the impact of industrial land transfer on carbon emissions in cities with different economic types. Bidding and auction transfer for industrial land in both economically developed and less developed cities can promote carbon pollution. While the more developed urban economy makes the intermediary effect of industrial structure not significant. In the future, it is necessary to strictly control the scale of industrial land supply; the whole process supervision mechanism of industrial land allocation and differentiated industrial land supply strategies will provide useful experience for many developing countries in allocating industrial land to mitigate carbon emissions, generating effective contributions to global carbon emission reduction.

Does the "Belt and Road Initiative" benefit the environment? Insight from analysis of intra-industry trade in environment goods.

The expansion of the Belt and Road Initiative (BRI) has raised a wide range of concerns about its environmental impact. Therefore, from the perspective of environmental impacts, this study used the two-way fixed effect staggered differences in differences (TWFE Staggered DID) method to examine the impact of the BRI on the Environment Goods (EGs) intra-industry trade (IIT) between China and other Belt and Road (B&R) countries, including a sample of 191 countries, covering the period from 2010 to 2019 for eliminating the impact of COVID-19 and the financial crisis in 2008 and 2009. Because only 135 countries signed a Memorandum of Understanding between 2010 and 2019, this study treated these B&R countries as the study group, and the other 73 countries (non-B&R countries) as the control group. This study described EGs using the 54 6-digit code Environment Goods in Harmonized Commodity Description and Coding System listed in the "APEC LIST OF ENVIRONMENT GOODS" published by the Asia-Pacific Economic Cooperation in 2012, and used the intra-industry trade index proposed by Grubel and Lloyd in 1971 to measuring dependent variable. The research results indicated that the BRI has significantly promoted bilateral EGs IIT. The mechanism test implied that, in addition to direct impacts, the BRI also has indirect impacts by boosting the energy restructuring of B&R countries. These results prove that the BRI has positive impacts on the environment. The heterogeneity test showed that there is a heterogeneous impact depending on the type of IIT, product categorization, B&R countries' income levels, and geographic environment. This study not only gives theoretical and empirical evidence of the positive environmental impacts of the BRI, but also provides practical guidance for the development of EGS IIT between China and B&R countries, thereby contributing to global carbon emissions reduction and environmental governance to some degree.

Internal revenue sharing methodology for virtual power plant clusters considering carbon incentive and penalty mechanisms

With the rapid development of renewable energy and the urgent need for global carbon emission reduction, virtual power plants have become a high-profile energy management model that can integrate multiple energy resources. How to effectively integrate renewable energy to reduce carbon emissions, how to optimize the use of different energy resources, and how to fairly distribute economic benefits within virtual power plant clusters while encouraging the reduction of carbon emissions are issues that need to be addressed in research. The study first established a virtual power plant model and conducted in-depth optimization for its economic and environmental indicators. Subsequently, the study constructed a game model within the virtual power plant cluster, aiming to solve the problem of income distribution in this diversified energy system. The research results found that commercial users have the highest carbon emissions, followed by industrial users, while residential users have the lowest carbon emissions. In terms of optimized user electricity consumption behavior, the peak-to-valley difference rate of industrial users has been reduced by 17%, and the daily load rate has increased by 6%; the peak-to-valley difference rate of commercial users has been reduced by 12%, and the daily load rate has increased by 6%; The peak-to-trough difference rate for residential users decreased by 8%, and the daily load rate increased by 4%. In addition, the research also proposes a method of internal revenue distribution of virtual power plant clusters based on a carbon reward and punishment mechanism, which provides a new way for the synergy effects and economic benefit distribution of virtual power plants. Research is of positive significance in solving pressing issues in the field of energy management and provides strong support for the development of future sustainable energy systems.

Evaluation of optimal waste lithium-ion battery recycling technology driven by multiple factors

As energy and environmental issues become increasingly prominent, the process of global carbon emission reduction is now underway. In the transportation sector, the application of electric vehicles is an effective way to achieve energy saving and emission reduction. Lithium-ion battery, an important component of electric vehicles, has received widespread attention for its advantages including small size, high energy density and smooth discharge voltage. However, their potential safety and pollution issues are becoming prominent and must be properly handled. Moreover, the technical route and future direction of LIB recycling are still unclear at this stage. Herein, this paper evaluates different waste lithium-ion battery recycling technologies in a multi-criteria decision framework to determine the best technology. A criteria system driven by multiple factors is established, including environmental impact (C1), technical risk (C2), comprehensive resource utilization (C3), resource consumption (C4) and economic performance (C5). Subsequently, since the optimal technology is novel and difficult to select, the hesitant fuzzy linguistic term sets are used to express the degree of hesitation in expert decision making. Finally, from the aspects of the choice of sorting method, the Tomada de decisao interativa multicriterio method based on the expectation effect is used to evaluate the optimal waste lithium-ion battery recycling technology, and the validity of the framework is illustrated by data analysis.

Green outward foreign direct investment and host country environmental effects: The home country's carbon emission reduction system is crucial

Studies on the environmental impacts of outward foreign direct investment (OFDI) with less attention paid to the relationship between gradually increasing green investment and carbon emissions in the host countries. Based on the background of global carbon emission reduction, this study constructs a STIRPAT and difference-in-differences model to analyze the environmental effects of China's OFDI in ASEAN countries using unique enterprise-level data of China's OFDI from 2005 to 2020. The results show that the impact of Chinese OFDI on carbon emissions in ASEAN countries varies with the investment sector, the home country's enterprise ownership type, and environmental policy. China's green OFDI shows a clear “Pollution Halo” effect regarding investment sectors. However, the “Pollution Halo” effect of green OFDI by Chinese state-owned enterprises (SOEs) is more pronounced when considering the home country's enterprise ownership type. In addition, from the home country's environmental policy perspective, implementing China's domestic low-carbon pilot policy has a “Pollution Haven” effect on host countries. This study provides policy implications to promote the green transformation of Chinese OFDI and carbon emission reduction in ASEAN countries.

Urban Building Energy Modeling with Parameterized Geometry and Detailed Thermal Zones for Complex Building Types

Urban building energy modeling (UBEM) has attracted wide attention to the requirement for global carbon emission reduction. This paper presents a UBEM tool, AutoBPS-Param, to generate building energy models (BEMs) with parameterized geometry and detailed thermal zones, especially for complex building types, considering the shading effect from surrounding buildings simultaneously. Three building number scales and four scenarios were analyzed in the hotel-related buildings in Changsha, China. For the prototype modeling of Scenario 1, eighteen prototype building energy models for six building types in three vintages were created, and their simulation results were aggregated based on their representative floor areas. For AutoBPS-Param of Scenario 4, the method created one EnergyPlus (Version: 9.3.0) model for each building. The geometry of the prototype model was scaled and modified based on the target building’s length, width, and number of stories. The surrounding buildings were also added to the AutoBPS-Param simulation to better capture the urban dynamic impact. The results showed that the annual electricity and natural gas energy use intensity (EUI) of the pre-2005 HotelOffice prototype model was 172.25 and 140.45 kWh/m2. In contrast, with the AutoBPS-Param method, the annual electricity EUIs of 71 HotelOffice buildings constructed before 2005 ranged from 159.51 to 213.58 kWh/m2 with an average of 173.14 kWh/m2, and the annual gas EUIs ranged from 68.02 to 229.12 kWh/m2 with an average of 108.89 kWh/m2. The proposed method can better capture the diversity of urban building energy consumption.

A method for generating complete EV charging datasets and analysis of residential charging behaviour in a large Norwegian case study

Electric vehicles (EVs) are part of the solution to achieve global carbon emissions reduction targets, and the number of EVs is increasing worldwide. Increased demand for EV charging can challenge the grid capacity of power distribution systems. Smart charging is therefore becoming an increasingly important topic, and availability of high-grade EV charging data is needed for analysing and modelling of EV charging and related energy flexibility. This study provides a set of methodologies for transforming real-world and commonly available EV charging data into easy-to-use EV charging datasets necessary for conducting a range of different EV studies. More than 35,000 residential charging sessions are analysed. The datasets include realistic predictions of battery capacities, charging power, and plug-in State-of-Charge (SoC) for each of the EVs, along with plug-in/plug-out times, and energy charged. Finally, we analyse how residential charging behaviour is affected by EV battery capacity and charging power. The results show a considerable potential for shifting residential EV charging in time, especially from afternoon/evenings to night-time. Such shifting of charging loads can reduce the grid burden resulting from residential EV charging. The potential for a single EV user to shift EV charging in time increases with higher EV charging power, more frequent connections, and longer connection times. The proposed methods provide the basis for assessing current and future EV charging behaviour, data-driven energy flexibility characterization, analysis, and modelling of EV charging loads and EV integration into power grids.

Digital transformation and manufacturing companies’ ESG responsibility performance

Digital transformation improves the efficiency and perception of companies. This paper investigates the impact of digital transformation in improving the environmental, social, and governance (ESG) responsibility performance of manufacturing companies in China. The results show that digital transformation can significantly improve manufacturing companies’ ESG responsibility performance. Every 1% increase in the degree of digital transformation will improve the ESG responsibility performance of manufacturing companies by 0.124%. Among the digital transformation techniques, digital strategy plays a more significant role than digital technology in improving manufacturing companies’ ESG responsibility performance. Total factor productivity, information transparency and investor stickiness moderate how digital transformation affects manufacturing companies’ ESG responsibility performance. The findings of this paper enrich and expand the research on the effects of digital transformation on manufacturing companies and provide a novel perspective to accelerate the progress of manufacturing companies worldwide in fulfilling ESG responsibilities and achieving global carbon emission reduction targets.

Emission Characteristics, Speciation, and Potential Environmental Risks of Heavy Metals from Coal-Fired Boilers: A Review

Coal-fired boilers, including coal-fired power plants (CFPPs) and coal-fired industrial boilers (CFIBs), are an important area for achieving sustainability globally as they are one of the globally important sources of anthropogenic emissions of heavy metals (HMs) due to huge amount of coal consumption. To date, the investigation of atmospheric emission characteristics, speciation, and potential environmental risks of HMs from coal-fired boilers has received widespread attention and achieved significant progress. To characterise the emissions of HMs from coal-fired boilers, research is currently being carried out in the areas of (1) studying the release of HMs from coal combustion processes, (2) developing emission factors and emission inventories, and (3) revealing the cross-media partitioning of HMs between different output streams. Research on the chemical forms of HMs in waste from coal-fired boiler is currently focused on chemical valence and speciation components. The sequential chemical extraction method is currently the most widely used method for investigating the chemical fractionations of HMs in wastes from coal-fired boilers. Studies indicate that different HM elements display differentiated characteristics of speciation in waste from coal-fired boilers. Early studies on potential environmental risk and ecological risk caused by HMs are usually based on actual monitoring values of HMs in the target environmental media. The risk assessment code method and the leaching toxicity method are the most widely used method to study the potential environmental risk of HMs in waste from coal-fired boilers. With the implementation of global carbon emission reduction strategies, the scale of coal-fired boilers and air pollution control technologies are bound to change in the future. Therefore, as an important component of global efforts to achieve sustainable development, more research is needed in the future to improve the accuracy of emission inventories, reveal the mechanisms of HM chemical transformation, and establish methods for potential environmental risk assessment at regional scales.

CARBON DIOXIDE UTILIZATION BY PRESSURIZED CONCRETE MIXER

Concrete production is one of the most power consuming processes in construction, which emits large amounts of CO2. The use of utilized carbon dioxide in concrete production can reduce the carbon trace of concrete production and therefore reduce its negative impact on the environment.
 The relevance of CO2 utilization in concrete production is due to several factors:
 1. Environmental issues: Concrete production is one of the largest sources of carbon emissions. Therefore, the search for ways to reduce carbon emissions, including by CO2utilization, is relevant and obligatory.
 2. Consumer Interest: An increasing number of consumers are oriented on environmentally friendly products and services, so the use of utilized CO2 in concrete production can increase the product attractiveness.
 3. Starting the transition to a low carbon emitted economy: Due to growing global trendto carbon emission reduction, many countries and organizations are transiting to a low carbon emitted economy. New technologies such as the use of utilized CO2 in concrete production can contribute to this transition.
 The aim of research: the development of a pressurized concrete mixer.
 Object of research: concrete mixture with CO2.
 The article describes the design, principle of operation and schemes of a concrete mixer operating under pressure. This device was designed at the Almetyevsk State Institute in the laboratory of the Center for Scientific and Technical Research to conduct an experiment on concrete carbonization.

The impact of manufacturing servitization on consumption-based carbon rebound effect: A global value chain research perspective

This study investigates the causal effect of manufacturing servitization on the consumption-based carbon rebound effect, employing the empirical approach based on the value-added trade and SBM-DEA model. The results show that improving the servitization level will lead to a significant decrease in the consumption-based carbon rebound effect of the global manufacturing industry. Moreover, the main paths through which the manufacturing servitization inhibits the consumption-based carbon rebound effect are the human capital and government management. We also find that the impact of manufacturing servitization is more substantial in advanced manufacturing and developed economies, which is smaller in manufacturing sectors with higher global value chain positions and lower export penetration levels. These findings suggest that improving the level of manufacturing servitization helps alleviate the consumption-based carbon rebound effect and is conducive to achieving the target of global carbon emission reduction.

An Improved Flower Pollination Algorithm based Modelling Method of Photovoltaic Modules

In recent years, with the acceleration of the global carbon emission reduction process, the sustainable development of clean energy has become the leading role, and the photovoltaic (PV) market has ushered in a hotter market. For accurately estimating the power generation of PV modules under different weather conditions, the modeling of solar cells or PV modules is very important. However, the conventional optimization algorithms cannot satisfy the accuracy of identification of the five parameters in the single diode model. In this paper, an improved FPA is proposed to estimate the five model parameters of the PV modules. In the proposed algorithm, the FPA is improved by considering different updating methods of solution for each pollen, with or without Levy flight. Experimental results validate that the proposed improved FPA can accurately identify the five model parameters and satisfy the modeling demands of PV arrays in engineering applications.

A short-term wind power prediction approach based on ensemble empirical mode decomposition and improved long short-term memory

Wind power plays a significant role in the reduction of global carbon emissions. Traditional prediction approaches sometimes struggle to meet the actual needs due to the non-linearity, and volatility of wind power. Hence, an integrated prediction model is proposed, which combines long short-term memory (LSTM), ensemble empirical mode decomposition (EEMD), and particle swarm optimization (PSO). Firstly, the actual series was decomposed using EEMD into several subseries components with varying frequencies to mitigate the impact of the series' non-smoothness on prediction accuracy. Next, to mitigate the issue of subjectivity in the manual tuning of parameters in the traditional LSTM model, PSO is utilized to optimize hyperparameter values for LSTM. After constructing the PSO-LSTM model for each subseries, the ultimate prediction results are obtained by aggregating the predicted values from each subseries. The simulation results show that the proposed model achieves higher accuracy and stability of prediction than other comparative models.

Effect of Flexible Operation on Residual Life of High-Temperature Components of Power Plants

Electricity generation from renewable energy sources is emerging as a result of global carbon emission reduction policies. However, most renewable energy sources are non-dispatchable and cannot be adjusted to meet the fluctuating electricity demands of society. A flexible operation process has been proposed as an effective solution to compensate for the unstable nature of renewable energy sources. Thermal load fluctuations during flexible operation may cause creep–fatigue damage to the high-temperature components of thermal power plants, as they are designed with a focus on creep damage under a constant power level. This study investigated the residual life of high-temperature components, such as a superheater tube and a reheater header, to failure under flexible operation conditions using finite element analysis and empirical models. First, we determined an analytical solution for the straightened superheater tube under thermal conditions and compared it with the numerical solution to verify the numerical models. Through the verified finite element model, the creep–fatigue life of the reheater header was estimated by considering flexible operation factors and employing the Coffin–Manson and Larson–Miller models. Although fatigue damage increases with decreasing minimum load and ramp rate, we confirmed that creep damage significantly affects the residual life during flexible operation. In addition, a surrogate model was proposed to evaluate the residual life of the reheater as a function of the flexible operation factors using the machine learning methodology, based on the results of finite element methods. It can be used to predict its residual life without performing complex thermo-structural analysis and relying on empirical models for fatigue and creep life. We expect our findings to contribute to the efficient operation of thermal power plants by optimizing the flexible operation factors.

Research on the indoor temperature regulation characteristics of a nearly zero energy building with the nonlinear heat capacity building components: A simple model

With establishment of global carbon reduction targets, energy conservation and emission reduction of building is imperative. By passive building design method, nearly zero energy buildings (nZEB) can decrease building energy consumption requirements. To reduce indoor temperature fluctuation of the nZEB in heating season, the conventional energy storage materials (CESM) or phase change materials (PCM) are utilized in the building components for thermal storage. The heat capacity of the PCM was simplified as ideal nonlinear heat capacity. To reveal the indoor temperature distribution characteristic in the nZEB affected by the building components with or without the nonlinear heat capacity, a simplified mathematical model was established. Several evaluation indicators were put forward to describe indoor temperature regulation difference between the CESM and the PCM. The results show that indoor temperature distribution characteristics of the nZEB with PCM was significantly different with that of the CESM building. The maximum indoor temperature distribution frequency of the PCM building is much larger than that of the CESM building. The maximum indoor temperature distribution frequency of PCM building was more concentrated and occurred close to the phase transition temperature. The maximum indoor temperature distribution frequency of the CESM is not exceed 10%, however, that of the PCM building can reach 42% in the same work condition. The maximum indoor temperature distribution frequency, the indoor comfort guarantee ratio and the latent heat utilization time ratio of the PCM building can be obviously increased by selecting the phase transition temperature close to the average indoor temperature in the heating season. The study work represent that the PCM building components with the nonlinear heat capacity can regulate the indoor temperature distribution frequency by change phase transition temperature.

Low-Carbon Energy Transition for the Sarawak Region via Multi-Period Carbon Emission Pinch Analysis

The Paris agreement in 2015 has required that countries commit to global carbon emission reduction by setting their national targets. In most countries, the electricity sector is identified as one of the major contributors to carbon emissions. Therefore, the governments count on decarbonizing the electricity sector to achieve their carbon reduction targets. However, this could be challenging as it is complex and involves multi-stakeholders in implementing the decarbonization plan. This work presents a mathematical optimization model to determine multi-period electricity generation planning to achieve the electricity demand and the carbon reduction target. A multi-period analysis allows long-term planning for decarbonizing the electricity sector by the gradual phasing out of coal-based power plants and the introduction of renewable-based electricity generation. To illustrate the proposed approach, the developed model is solved to strategize low-carbon energy transition planning for the Sarawak region in Malaysia. The model determines the optimal amount of new renewables required during each of the time periods, from 2020–2040, to meet the carbon reduction target. The optimal results are generated under two scenarios—no co-firing and co-firing. The generated results show that the co-firing scenario resulted in a 14.09% reduction in new renewable additions and a 5.78% reduction in the total costs. The results also determined a 66% reduction in coal consumption in 2050 when compared to the base year in 2020.

How does agricultural production agglomeration affect green total factor productivity?: empirical evidence from China

Economic growth in agriculture is often accompanied by environmental pollution. To overcome this dilemma, agricultural green total factor productivity is essential. The role of agricultural production agglomeration has been studied extensively; however, there is no apparent association between agricultural production agglomeration and agricultural green total factor productivity. We use the non-angular and non-radial super-efficiency slacks-based measure model and the global Malmquist-Luenberger productivity model to assess the agricultural green total factor productivity in 30 Chinese provinces from 2001 to 2019. In addition, we investigate the evolutionary trend, influence relationship, and internal mechanism between agricultural production agglomeration and agricultural green total factor productivity. The results show an inverted U-shaped relationship between agricultural production agglomeration and agricultural green total factor productivity. Heterogeneity analysis finds that this role is more pronounced in provinces located inthe Yangtze River economic zone with strong fiscal support for agriculture and high levels of regional economic development. Regarding the mechanism analysis, enhancing green technology progress and the quality of rural human capital leads to an increase in agricultural green total factor productivity. Therefore, this study provides a new perspective on global carbon emission reduction and a theoretical and practical reference for realizing global agricultural green development.