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Plants remove carbon dioxide from the atmosphere through photosynthesis. Because agriculture’s productivity is based on this process, a combination of technologies to reduce emissions and enhance soil carbon storage can allow this sector to achieve net negative emissions while maintaining high productivity. Unfortunately, current row-crop agricultural practice generates about 5% of greenhouse gas emissions in the United States and European Union. To reduce these emissions, significant effort has been focused on changing farm management practices to maximize soil carbon. In contrast, the potential to reduce emissions has largely been neglected. Through a combination of innovations in digital agriculture, crop and microbial genetics, and electrification, we estimate that a 71% (1,744 kg CO₂e/ha) reduction in greenhouse gas emissions from row crop agriculture is possible within the next 15 y. Importantly, emission reduction can lower the barrier to broad adoption by proceeding through multiple stages with meaningful improvements that gradually facilitate the transition to net negative practices. Emerging voluntary and regulatory ecosystems services markets will incentivize progress along this transition pathway and guide public and private investments toward technology development. In the difficult quest for net negative emissions, all tools, including emission reduction and soil carbon storage, must be developed to allow agriculture to maintain its critical societal function of provisioning society while, at the same time, generating environmental benefits.

Though interfirm collaboration on carbon emission reduction, the cross-enterprise flow of emission reduction resources and improved efficiency in greenhouse gas reduction can be realized. Especially in the context of big data, enterprises can find suitable partners for emission reduction faster and more accurately through interfirm collaboration. However, similar to other cooperative modes, revenue allocation is the key to ensuring the stability of the collaborative emission reduction system. Based on the premise of carbon trading, this paper discusses revenue allocation among enterprises participating in the collaborative emission reduction process under complete information in a big data context. Specifically, we constructed a Shapley value analysis model of revenue allocation for interfirm collaboration on carbon emission reduction, and amended this model with investment cost and risk-bearing. Consequently, this research provides not only a theoretical basis for solving the problem of revenue distribution in the process of collaborative emission reductions among enterprises but also a theoretical guide for enterprises countermeasures following the completion of China's future carbon trading mechanism.

Owing to the rising concerns about environmental degradation worldwide, firms in several developed and developing countries are pursuing carbon emission reduction targets. In addition, in recent years, there is evidence of a shift in consumer preferences in favour of low-carbon products. Using a theoretical model, where the shift in consumer preferences is explicitly incorporated, we evaluate the impact of carbon emission reduction cost-sharing on supply chain profit. In our model, consumers are willing to pay a higher price for low-carbon products and hence the retailer considers sharing the cost of carbon emission reduction with the manufacturer. Our model also includes a carbon trading mechanism. We identify a range of carbon emission reduction cost-sharing such that both supply chain enterprises are better-off. We find that, while achieving the aim of carbon emission reduction, consumer preference for low-carbon products can benefit both supply chain enterprises. Numerical simulations show that carbon emission reduction cost-sharing increases the retailer’s order quantity as well as the profit and hence there is an incentive for the two supply chain enterprises to cooperate.

The intensification of the global climate crisis has thrust the imperative of controlling greenhouse gas emissions into the spotlight, commanding the attention of individuals, industries, and nations alike. Reducing carbon emissions and maximizing carbon utilization have assumed paramount significance in the contemporary industrial landscape. Within this overarching context, Carbon Capture, Utilization, and Storage (CCUS) technology has emerged as a transformative and pivotal means of addressing the multifaceted challenges posed by escalating emissions.Among the diverse CCUS methodologies, enhanced oil recovery (EOR) has distinguished itself as an up-and-coming technique, offering economic viability and environmental impact. Simultaneously, enhanced gas recovery (EGR) has recently gained momentum due to its remarkable potential as a negative carbon technology.This study employs an integrated approach to gain a deeper and more precise understanding of how reservoir heterogeneity influences the geological utilization of CO2.It commences with the utilization of FLAC3D and the \"gast\" tool in R language to generate comprehensive data fields that quantitatively characterize heterogeneity in terms of porosity standard deviation and correlation length. Subsequently, the research conducts a comprehensive and methodical analysis of how heterogeneity impacts CO2 gas displacement.

Carbon emission reduction is crucial for mitigating global climate change, and green fiscal policies, through providing economic incentives and reallocating resources, are key means to achieve carbon reduction targets. This paper uses data covering 248 cities from 2003 to 2019 and applies a multi-period difference-in-differences model (DID) to thoroughly assess the impact of energy conservation and emission reduction ( ECER ) fiscal policies on enhancing carbon emission ( CE 1 ) reduction and carbon efficiency ( CE 2 ). It further analyzes the mediating role of Green Innovation ( GI ), exploring how it strengthens the impact of ECER policies. We find that: (1) ECER policies significantly promote the improvement of carbon reduction and CE 2 , a conclusion that remains robust after excluding the impacts of concurrent policy influences, sample selection biases, outliers, and other random factors. (2) ECER policies enhance CE 1 reduction and CE 2 in pilot cities by promoting green innovation, and this conclusion is confirmed by Sobel Z tests. (3) The effects of ECER policies on CE 1 reduction and the improvement of CE 2 are more pronounced in higher-level cities, the eastern regions and non-resource cities. This research provides policy makers with suggestions, highlighting that incentivizing green innovation through green fiscal policies is an effective path to achieving carbon reduction goals.

In this study, an improved gravity model and social network analysis (SNA) are applied to analysis CO 2 emissions in China’s power sector, uniquely incorporating electricity and fossil fuel trade flows. It further explores the dynamic effect of energy transition on networks using a panel model, and clarifies the provincial roles in emission abatement and resource allocation. According to the findings, significant regional heterogeneities in CO 2 emissions from 2007 to 2022 can be observed. Coal-dependent provinces, such as Inner Mongolia and Shanxi, face high emissions and challenging transitions, while developed areas such as Beijing and Shanghai have decreased emissions through clean energy integration and enhanced power efficiency. Network analysis identifies Beijing and Jiangsu as central to resource management, empowered by robust policy and information-sharing capabilities, while most provinces demonstrate weaker coordination owing to constrained intermediary functions. In addition, the study observes that energy transitions increase network density (0.3512) and contacts (0.3545) yet decrease efficiency (− 0.1464), suggesting technical and coordinative obstacles. An increasing degree of transition strengthens interprovincial CO 2 connections, establishing provinces experiencing more rapid transitions as critical nodes. Greater closeness centrality (0.0186) signifies shorter collaborative pathways, accelerating the transition. These findings derive practical guidance for regional power collaborations and sustainable growth, offering novel perspectives for a green transition toward carbon neutrality.

The common root characteristic of CO2 and air pollutants in the power industry, both derived from fossil fuel combustion, provides a natural basis for their synergistic emission reduction. However, existing studies suffer from the lack of a multi-pollutant synergistic evaluation system and an imperfect emission reduction technology database, which hinder their ability to support low-cost and high-efficiency emission reduction practices in the industry. Targeting the minimization of synergistic emission reduction costs and the maximization of emission reduction effects, this study integrated the process and economic parameters of 11 power generation technologies and 55 pollutant control technologies to establish a full-chain energy conservation and emission reduction technology database for the power industry, through literature research, industry surveys, and data mining. Based on the definition of pollution equivalent in the Environmental Protection Tax Law, we innovatively developed an air pollutant equivalent normalization evaluation method and constructed a two-dimensional coordinate system comprehensive evaluation system for CO2 and air pollutants, enabling quantitative analysis and visual evaluation of the synergistic emission reduction effects of various technologies. The results show that new energy power generation technologies such as nuclear power and wind power, as well as O2/CO2 cycle combustion, ammonia-based desulfurization, and SNCR-SCR combined reduction technologies, exhibit excellent synergistic emission reduction performance for CO2 and multiple pollutants. In contrast, some conventional pollutant control technologies, such as the limestone-gypsum method and traditional electrostatic precipitation, have significant CO2 emission increase antagonistic effects. This study also completed the two-dimensional classification of 66 emission reduction technologies based on “emission reduction efficiency-economic cost”, identified application scenarios for different types of technologies, and proposed optimized paths for synergistic emission reduction adapted to the development of the power industry. The research findings fill the gap in quantitative standards for multi-pollutant synergistic emission reduction, provide theoretical support and detailed technical references for emission reduction technology selection and environmental policy formulation in the power industry, and help the industry achieve the dual development requirements of the “double carbon” goal and air quality improvement.

The synergetic reduction of CO2 and PM2.5 emissions has received much attention in China in recent years. A comprehensive evaluation of the synergy between CO2 emission reduction (CER) and PM2.5 emission reduction (PER) would provide valuable information for developing synergetic control policies. Thus, we constructed a comprehensive CO2-PM2.5-emission-reduction index system and evaluated the synergy between CER and PER, using the coupling coordination degree (CCD) and relative development degree (RDD) model in China’s 329 cities from 2003 to 2017. The spatiotemporal characteristics of the CCD were analyzed on the national, regional, and urban scales. Furthermore, we used the spatial autocorrelation analysis, kernel density estimation, and Dagum Gini coefficient to investigate the spatial autocorrelation, evolutionary characteristics, and regional differences of the CCD. The results indicate that (1) the synergy between CO2 and PM2.5 emissions’ reductions showed an upward trend, and the lowest CCD values occurred in NW and Shanghai on the regional and urban scales, respectively; (2) the CCD showed obvious spatial clustering characteristics, with 75% of the cities located in the “High–High” or “Low–Low” clustering zones in the Moran scatter plots in 2017; (3) the polarization of CCD in SC, MYR, and SW showed intensified trends; (4) and the hypervariable density was the largest contributor to the overall difference in the CCD. Our findings suggest that more attention should be paid to the top-level design of the policies, technological innovation, and cross-regional or intercity cooperation.

This research investigates the application of machine learning models to optimise renewable energy systems and contribute to achieving Net Zero emissions targets. The primary objective is to evaluate how machine learning can improve energy forecasting, grid management, and storage optimisation, thereby enhancing the reliability and efficiency of renewable energy sources. The methodology involved the application of various machine learning models, including Long Short-Term Memory (LSTM), Random Forest, Support Vector Machines (SVMs), and ARIMA, to predict energy generation and demand patterns. These models were evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Key findings include a 15% improvement in grid efficiency after optimisation and a 10–20% increase in battery storage efficiency. Random Forest achieved the lowest MAE, reducing prediction error by approximately 8.5%. The study quantified CO2 emission reductions by energy source, with wind power accounting for a 15,000-ton annual reduction, followed by hydropower and solar reducing emissions by 10,000 and 7500 tons, respectively. The research concludes that machine learning can significantly enhance renewable energy system performance, with measurable reductions in errors and emissions. These improvements could help close the “ambition gap” by 20%, supporting global efforts to meet the 1.5 °C Paris Agreement targets.

As the carbon trading scheme has a significant impact on China’s sustainable economy and environmental protection, the policy influence of carbon emissions and carbon emission efficiency in pilot provinces has become a key research topic. Based on the data of 30 provinces and cities in China from 2007 to 2018, this paper estimates carbon emission efficiency by using a super-efficiency SBM model, and the difference-in-difference method is adopted to investigate the policy’s influence. The results show that: (1) carbon trading policies have a significant carbon emission reduction effect and a positive effect on carbon emission efficiency in pilot areas. (2) There is a dynamic effect that increases year by year, and the policies have a synergistic emission reduction effect on CO2 and SO2. (3) The carbon trading policy has different effects on carbon emission efficiency depending on pilot areas. Before and after the implementation of the policy, carbon emission efficiency in Tianjin remained almost unchanged, while the carbon emission efficiency in Hubei and Chongqing increased significantly. Although the efficiency of Shanghai and Guangdong remains at the forefront, they fluctuate greatly. Beijing is the only city to remain a frontier every year, showing significant policy impact.