Search Results (210)

To accurately analyze the dynamic response and driving mechanism of forest carbon sequestration in the core area of the Loess Plateau’s Returning Farmland to Forestry Project, this study takes the Beiluo River Basin as the research area. Using spatial autocorrelation, gravity model, a geodetector, and spatiotemporal geographically weighted regression models, it analyzes the spatiotemporal evolution of forest carbon sequestration and the spatial heterogeneity of its influencing factors based on 2000–2023 data. The results show the following: (1) Forest carbon sequestration in the basin increased by 13.55% from 2000 to 2023; its spatial pattern shifted from “middle reaches concentration” to “stable middle reaches core plus significant upper reaches growth”, with the gravity center moving “southeast then northwest”. (2) Forest carbon sequestration had significant positive spatial correlation, with hotspots in soil–rock mountain forest areas and cold spots in ecologically fragile or high-human-activity areas. (3) Natural ecological factors dominated forest carbon sequestration evolution, socioeconomic factors enhanced synergy, and evapotranspiration and NDVI had significant impacts. (4) Factor impacts had spatiotemporal heterogeneity, such as the decaying positive effect of precipitation and the “positive-negative-equilibrium” change in forestry value-added. This study provides scientific guidance for basin and Loess Plateau ecological restoration and “double carbon” goal achievement. Full article

The rapid advancement of technologies such as artificial intelligence, big data, and cloud computing has driven continuous expansion of global data centers, resulting in increasingly severe energy consumption and carbon emission challenges. According to projections by the International Energy Agency (IEA), the global electricity demand of data centers is expected to double by 2030. The construction of green data centers has emerged as a critical pathway for achieving carbon neutrality goals and facilitating energy structure transition. This paper presents a systematic review of the role of waste heat recovery technologies in data centers for achieving low-carbon development. Categorized by aspects of waste heat recovery technologies, power production and district heating, it focuses on assessing the applicability of heat collection technologies, such as heat pumps, thermal energy storage and absorption cooling, in different scenarios. This study examines multiple electricity generation pathways, specifically the Organic Rankine Cycle (ORC), Kalina Cycle (KC), and thermoelectric generators (TEG), with comprehensive analysis of their technical performance and economic viability. The study also assesses the feasibility and environmental advantages of using data center waste heat for district heating. This application, supported by heat pumps and thermal energy storage, could serve both residential and industrial areas. The study shows that waste heat recovery technologies can not only significantly reduce the Power Usage Effectiveness (PUE) of data centers, but also deliver substantial economic returns and emission reduction potential. In the future, the integration of green computing power with renewable energy will emerge as the cornerstone of sustainable data center development. Through intelligent energy management systems, cascaded energy utilization and regional energy synergy, data centers are poised to transition from traditional “energy-intensive facilities” to proactive “clean energy collaborators” within the smart grid ecosystem. Full article

With the goal of achieving carbon neutrality, promoting the clean and low-carbon transformation of energy assets, as exemplified by existing thermal power units, has emerged as a pivotal challenge in addressing climate change and achieving sustainable development. Arrangements and technologies such as the electricity–carbon–certificate multi-market, microgrids with direct green power connections, and carbon capture and storage (CCS) retrofitting provide favorable conditions for facing the aforementioned challenge. Based on an analysis of how liquid-storage CCS retrofitting affects the flexibility of thermal power units, this manuscript proposes a bi-level optimization model and solution method for capacity allocation for grid-connected microgrids, while considering CCS retrofits under multi-markets. This approach overcomes two key deficiencies in the existing research: first, neglecting the relationship between electricity–carbon coupling characteristics and unit flexibility and its potential impacts, and second, the significant deviation of scenarios constructed from real policy and market environments, which limits its ability to provide timely and relevant references. A case study in southern China demonstrates that first, multi-market implementation significantly boosts microgrids’ investment in and absolute consumption of renewable energy. However, its effect on reducing carbon emissions is limited, and renewable power curtailment may surge, potentially deviating from the original intent of carbon neutrality policies. In this case study, renewable energy installed capacity and consumption rose by 17.09% and 22.64%, respectively, while net carbon emissions decreased by only 3.32%, and curtailed power nearly doubled. Second, introducing liquid-storage CCS, which decouples the CO₂ absorption and desorption processes, into the capacity allocation significantly enhances microgrid flexibility, markedly reduces the risk of overcapacity in renewable energy units, and enhances investment efficiency. In this case study, following CCS retrofits, renewable energy unit installed capacity decreased by 24%, while consumption dropped by only 7.28%, utilization hours increased by 22%, and the curtailment declined by 78.05%. Third, although CCS retrofitting can significantly reduce microgrid carbon emissions, factors such as current carbon prices, technological efficiency, and economic characteristics hinder large-scale adoption. In this case study, under multi-markets, CCS retrofitting reduced net carbon emissions by 86.16%, but the annualized total cost rose by 3.68%. Finally, based on the aforementioned findings, this manuscript discusses implications for microgrid development decision making, CCS industrialization, and market mechanisms from the perspectives of research directions, policy formulation, and practical work. Full article

This study aims to explore the role of government environmental auditing in promoting China’s coordinated goals of “pollution reduction, carbon mitigation, ecological expansion, and growth.” By analyzing 1959 panel data from 227 prefecture-level cities in China between 2011 and 2022, a four-dimensional evaluation framework was constructed, and empirical testing was carried out using a double machine learning method. The results indicate that environmental auditing significantly enhances the synergy of environmental governance, mainly by raising public environmental awareness, promoting industrial clustering, and fostering green innovation. Additionally, green finance provides complementary support to this process. This effect is particularly pronounced in regions with higher levels of marketization, more developed financial technology, and greater environmental expenditure. Based on these findings, this study concludes that environmental auditing plays a crucial role in promoting China’s coordinated goals of “pollution reduction, carbon mitigation, ecological expansion, and growth.” In particular, environmental auditing demonstrates its institutional value in promoting sustainable governance, especially in developing economies. Full article

In recent years, China’s economic and social development has faced challenges such as urban-rural imbalance and ecological pressure. Digital inclusive finance and ecological resilience have become key concerns in academia and policymaking. This study empirically examines whether digital inclusive finance can enhance ecological resilience and its underlying mechanisms, drawing on quantitative evidence from provincial panel data covering 2011–2020. By providing robust empirical results, it contributes to understanding the role of digital finance in supporting high-quality growth and ecological civilization. While the findings align with national strategies such as the “dual carbon” goal and rural revitalization, the study’s primary contribution lies in advancing interdisciplinary exploration through rigorous evidence rather than solely at the policy level. By constructing a double fixed effects model and panel data from 30 Chinese provinces (2011–2020), the study finds that digital inclusive finance significantly enhances ecological resilience, both directly and indirectly through channels such as environmental regulation, artificial intelligence development, and green credit. Moreover, its ecological impact is moderated by regional economic levels and digital infrastructure, with stronger effects observed in eastern and digitally advanced regions. In summary, this study reveals the mechanisms through which digital inclusive finance promotes ecological resilience, offering a theoretical foundation and practical guidance for policy formulation. Its key contribution lies in systematically analyzing the link between digital inclusive finance and ecological resilience, enriching the theoretical framework and providing data support for policy optimization and financial institutions’ strategic adjustments. Future efforts should focus on strengthening policy coordination to enhance the ecological role of digital finance, promoting financial innovation to support resilience, and advancing regional coordination to narrow the digital divide and achieve shared ecological protection. Full article

Driven by the “double carbon” goals, the penetration rate of distributed photovoltaics (PV) in distribution networks has increased rapidly. However, the continuous growth of distributed PV installed capacity poses significant challenges to the carrying capacity of distribution networks. Reinforcement learning (RL), with its capability to handle high-dimensional nonlinear problems, plays a critical role in analyzing the carrying capacity of distribution networks. This study constructs an evaluation model for distributed PV carrying capacity and proposes a corresponding quantitative evaluation index system by analyzing the core factors influencing it. An optimization scheme based on deep reinforcement learning is adopted, introducing the Deep Deterministic Policy Gradient (DDPG) algorithm to solve the evaluation model. Finally, simulations on the IEEE-33 bus system validate the good feasibility of the reinforcement learning approach for this problem. Full article

In the context of China’s “double carbon” goals, examining the spatial–temporal characteristics and influencing factors of the synergistic effect of pollution control and carbon reduction (SEPCR) in the Chengdu–Chongqing region (CCR) is crucial for advancing both air pollution (AP) control and carbon emissions (CE) mitigation. This study uses data on AP and CE from 2007 to 2022 and employs the coupling coordination degree (CCD) model, spatial autocorrelation analysis, and kernel density estimation to investigate the spatial–temporal distribution and dynamic evolution of the CCD between AP and CE in the CCR. Additionally, the Tobit regression model is applied to identify the key factors influencing this synergy. The results indicate that (1) during the study period, the air pollutant equivalents (APE) in the CCR showed a declining trend, while CE continued to increase; (2) the overall level of coupling coordination remained low, exhibiting an evolutionary pattern of initial increase, subsequent decrease, and then recovery, with synergistic effects showing slight improvement but significant fluctuations; (3) the SEPCR in the CCR was generally dispersed, exhibiting no significant spatial autocorrelation. A “core–periphery” structure emerged, with Chongqing and Chengdu as the core and peripheral cities forming low-value zones. Low–low clusters indicative of a “synergy poverty trap” also appeared; (4) economic development (PGDP), openness level (OP), and environmental regulation intensity (ER) are significant positive drivers, while urbanization rate (UR), industrial structure upgrading (IS), and energy consumption intensity (EI) exert significant negative impacts. Full article

The wide application of natural gas will help to achieve the goal of double carbon. The potential of energy saving and carbon reduction after deep condensation of a natural gas flue gas can reach as much as 10~12%. In this paper, the interplay between sensible and latent heat transfer in the process of condensation heat transfer was explored by adjusting the flue gas temperature, relative humidity, cooling water temperature, and other parameters entering the condensation heat exchanger by the condensation heat transfer experimental platform of a gas-fired boiler. The Ln number presents the proportion of water vapor condensation in flue gas, and the P number shows the total amount of water vapor condensation. The increase in Ln and p values promotes the enhancement of water vapor condensation, and the condensation heat transfer coefficient can reach about three to eight times that of a sensible heat transfer coefficient. As the Ln number increases from 0 to 0.35, the promoting effect of sensible heat is enhanced, up to a maximum of 2.5 times. However, from 0.35 to 0.75, the promotion gradually weakens. When the Ln number exceeds 0.75, sensible heat transfer begins to be suppressed, with a minimum coefficient of 0.7. The correction term of the sensible and latent heat transfer coefficient is added to the existing empirical correlation of pure convection heat transfer, which is applicable to various heat exchanger structures and verified by experiments. The micro-element superposition calculation method of the condensing heat exchanger is proposed to realize the digital accurate design of the condensing heat exchanger, which lays the foundation for the extensive promotion and application of the flue gas condensing heat exchanger. Full article

Over the last few decades, polymer composites have been rapidly making inroads in critical applications of electrical storage devices such as batteries and supercapacitors. Structural supercapacitor composites (SSCs) have emerged as multifunctional materials capable of storing energy while bearing mechanical loads, offering lightweight and compact solutions for energy systems. This study investigates the functionalization of Bisphenol A-based thermosetting polymers with ionic liquids, aiming to synthesize dual-functional structural electrolytes for SSC fabrication. A multifunctional sandwich structure was subsequently fabricated, in which the fabricated SSC served as the core layer, bonded between two structurally robust outer skins. The core layer was fabricated using carbon fibre layers coated with 10% graphene nanoplatelets (GNPs), while the skin layers contained 0.25% GNPs dispersed in the resin matrix. The developed device demonstrated stable operation up to 85 °C, achieving a specific capacitance of 57.28 mFcm⁻² and an energy density of 179 mWhm⁻² at room temperature. The performance doubled at 85 °C, maintaining excellent capacitance retentions across all experimented temperatures. The flexural strength of the developed sandwich SSC at elevated temperature (at 85 °C) was 71 MPa, which exceeds the minimum requirement for roofing sheets as specified in Australian building standard AS 4040.1 (Methods of testing sheet roof and wall cladding, Method 1: Resistance to concentrated loads). Finite element analysis (FEA) was performed using Abaqus CAE to evaluate structural integrity under mechanical loading and predict damage initiation zones under service conditions. The simulation was based on Hashin’s failure criteria and demonstrated reasonable accuracy. This research highlights the potential of multifunctional polymer composite systems in renewable energy infrastructure, offering a robust and energy-efficient material solution aligned with circular economy and sustainability goals. Full article

In the context of the “Dual Carbon” goals and ecological civilization development, enhancing forestry ecological total factor productivity (FETFP) has become vital for advancing green development and environmental governance. Confronted with tightening resource constraints and pressure to transform traditional growth models, whether digital intelligence integration can effectively empower improvements in FETFP requires in-depth empirical validation. Based on publicly available panel data from 30 Chinese provinces spanning 2012 to 2022, this study constructs an index system for measuring digital intelligence integration and FETFP. Using the Double Machine Learning (DML) framework, the study empirically identifies the impact of digital intelligence development on FETFP and explores its internal mechanisms. The key results show that (1) digital intelligence integration significantly enhances FETFP. For every unit increase in digital and intelligent integration, FETFP rises by an average of 19.97%; (2) mechanism analysis reveals that digital intelligence improves FETFP by optimizing the forestry industrial structure, promoting green technological innovation, and amplifying the synergistic effects of fiscal support; (3) and heterogeneity analysis suggests that the positive impact of digital intelligence integration is more pronounced in regions with higher environmental expenditures and stronger green finance support. Accordingly, this study proposes several policy recommendations, including accelerating digital infrastructure development, strengthening foundational digital intelligence capabilities, enhancing support for green innovation, leveraging the ecological multiplier effects of digital transformation, tailoring digital strategies to local conditions, and improving the precision of regional environmental governance. The findings provide robust empirical evidence for improving FETFP in developing and developed economies. Full article

Developing a more productive, resource-efficient, and climate-smart rubber agroforestry model is essential for the sustainable growth of natural rubber cultivation. In this study, we evaluated whether a double-row rubber plantation intercropped with the medicinal crop Ficus hirta Vahl. (DR-F) could achieve this goal, using a single-row rubber plantation (SR) as the control. We assessed the feasibility of the DR-F system based on productivity, solar utilization efficiency (SUE), partial factor productivity of applied nitrogen (PFPN), carbon efficiency (CE), net ecosystem carbon balance (NECB), and carbon footprint (CF). No significant difference was observed in rubber tree biomass between the DR-F (10.49 t·ha⁻¹) and SR (8.49 t·ha⁻¹) systems. However, the DR-F system exhibited significantly higher total biomass productivity (23.34 t·ha⁻¹) than the SR systems due to the substantial contribution from intercropped Ficus hirta Vahl., which yielded 12.84 t·ha⁻¹(p < 0.05). The root fresh weight yield of Ficus hirta Vahl. reached 17.55 t·ha⁻¹, generating an additional profit of 20,417 CNY ha⁻¹. The DR-F system also exhibited higher solar radiation interception and greater availability of soil nutrients. Notably, the roots of rubber trees and Ficus hirta Vahl. did not overlap at a 4 m distance from the rubber trees. The DR-F system achieved higher SUE (0.64%), PFPN (51.40 kg·kg⁻¹ N), and CE (6.93 kg·kg⁻¹ C) than the SR system, with the SUE and PFPN differences being statistically significant (p < 0.05). Although the NECB remained unaffected, the DR-F system demonstrated significantly higher productivity and a substantially lower CF (0.33 kg CO₂·kg⁻¹, a 56% reduction; p < 0.05). In conclusion, the DR-F system represents a more sustainable and beneficial agroforestry approach, offering improved productivity, greater resource use efficiency, and reduced environmental impact. Full article

The challenge of excess water production in mature heavy oil reservoirs presents significant environmental and economic concerns. This study evaluates the effectiveness of inflow control devices (ICDs) and autonomous inflow control devices (AICDs) for managing water production in heavy oil reservoirs with strong aquifer drives. Our investigation comprises two field implementations and a computational fluid dynamics (CFD) study. In the first field implementation, both ICDs and AICDs achieved substantial water reduction (25% and 32%, respectively) compared to conventional slotted liner completions, with ICDs demonstrating superior oil production performance, extending well life by approximately 30% and doubling accumulated oil. The second field implementation featured rate-controlled production (RCP) devices, showing that two AICD wells together produced 60% more accumulated oil and 40% less water than a single conventional well, effectively relieving surface facility bottlenecks. Full 3D Navier–Stokes simulations for a third field implementation revealed that passive ICDs outperformed AICDs under specific draw-down and spacing conditions, challenging the industry preference for newer technologies. The study’s findings, which include quantifiable reductions in the carbon footprint associated with decreased power consumption, provide valuable insights for operators seeking to optimize water management while minimizing environmental impact, advancing the sustainable oil production practices aligned with UN Sustainable Development Goals 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure), and 13 (Climate Action). Full article

Under the “double security” goal of achieving both food security and ecological protection, this study explores the green and low-carbon utilization efficiency of cultivated land (GLCUECL) in the Huaihe River Ecological Economic Belt (HREEB). This study identifies the spatiotemporal evolution characteristics and trends, promoting the green, low-carbon, and sustainable utilization of arable land resources in the HREEB, thus contributing to regional and national food and ecological security. Using a global super-efficiency EBM framework that accounts for undesirable outputs, as well as the GML index, the researchers measured and decomposed the GLCUECL in 25 prefecture-level cities of the HREEB from 2005 to 2021. The Theil index and kernel density estimation were applied to analyze regional disparities and changing developmental traits. Spatial convergence and divergence were assessed using the coefficient of variation and spatial convergence models. Key findings include the following: (1) Over time, the GLCUECL in the HREEB exhibited an overall upward trend and a non-equilibrium characteristic, namely the “East Sea-river-lake Linkage Area (ESLA) > Midwest Inland Rising Area (MIRA) > Huaihe River Ecological Economic Belt (HREEB) > North Huaihai Economic Zone (NHEZ)”. The increase in the GML index of the GLCUECL is mainly attributable to a technical progress change. (2) The overall difference in the GLCUECL tends to decline, which is mainly attributable to the intra-regional differences. (3) The overall kernel density curves for the HREEB and its three sub-regions exhibited a “rightward shift” trend. Except for the expansion and polarization of the absolute difference in the GLCUECL in the NHEZ, the absolute difference in GLCUECL in other regions, such as the HREEB, ESLA, and MIRA, exhibited a decreasing trend. (4) Spatial convergence analysis revealed that only the NHEZ lacks σ-convergence, whereas all regions exhibited β-convergence. Moreover, factors such as rural economic development level, cultivated land resource endowment, agricultural subsidy policy, crop planting structure, and technological input exerted a heterogeneous effect on the change in the GLCUECL. Based on these findings, this study offers recommendations for improving GLCUECL in the HREEB. Our recommendations include the implementation of the concept of green new development, optimization of the institution supply, establishing a regional cooperation mechanism for green and low-carbon utilization of cultivated land, and formulation of differentiated paths for improving the green and low-carbon utilization efficiency of cultivated land according to local conditions. Full article

Against the backdrop of the deepening implementation of the “Double Carbon” goals, reducing carbon emissions poses great pressure on China. As major agricultural and industrial provinces, the industrial structure of the three northeastern provinces has a crucial impact on carbon emissions. In order to explore this phenomenon, this study employed provincial and municipal data from 2007 to 2019 to simulate the spatial and temporal patterns of carbon emissions and GDP in Northeast China. The Tapio decoupling model was applied to assess the elasticity coefficient between economic development and carbon emissions, while the Theil index was used to evaluate the rationalization of the industrial structure. Then, a multiple linear regression model (MLR) was innovatively applied to explore the relationship between the indexes of the two models. This study found that carbon emissions and GDP in the three provinces both exhibited the characteristic of Liaoning > Heilongjiang > Jilin. In the decoupling analysis, 64.7% of the cities were dominated by benign decoupling. The negative decoupling areas were primarily composed of industrial cities in the southwest and resource-based cities in the east. In the rationalization analysis, there were large-scale irrational areas in 2019, which were concentrated in northwestern and southwestern industrial cities, and occasionally in eastern resource-based cities. There was a certain degree of spatial overlap between these two problematic areas. The MLR result showed that there was a positive correlation between the elasticity coefficient and the Theil index, indicating that optimizing the industrial structure can promote the upgrading of the decoupling status toward strong decoupling. This study provided a theoretical basis for improving the decoupling of carbon emissions and economic development through industrial structure rationalization. For overlapping regions, emission reduction can be prioritized through the rationalization of the industrial structure to achieve a better decoupling status. Full article

The global climate crisis has driven unprecedented agreements among nations on carbon mitigation. With China’s commitment to carbon peaking and carbon neutrality targets, the building sector has emerged as a critical focus for emission reduction, particularly because office buildings account for over 30% of building energy consumption. However, a systematic and regionally adaptive low-carbon technology evaluation framework is lacking. To address this gap, this study develops a multidimensional decision-making system to quantify and rank low-carbon technologies for office buildings in Beijing. The method includes four core components: (1) establishing three archetypal models—low-rise (H ≤ 24 m), mid-rise (24 m < H ≤ 50 m), and high-rise (50 m < H ≤ 100 m) office buildings—based on 99 office buildings in Beijing; (2) classifying 19 key technologies into three clusters—Envelope Structure Optimization, Equipment Efficiency Enhancement, and Renewable Energy Utilization—using bibliometric analysis and policy norm screening; (3) developing a four-dimensional evaluation framework encompassing Carbon Reduction Degree (CRD), Economic Viability Degree (EVD), Technical Applicability Degree (TAD), and Carbon Intensity Degree (CID); and (4) conducting a comprehensive quantitative evaluation using the AHP-entropy-TOPSIS algorithm. The results indicate distinct priority patterns across the building types: low-rise buildings prioritize roof-mounted photovoltaic (PV) systems, LED lighting, and thermal-break aluminum frames with low-E double-glazed laminated glass. Mid- and high-rise buildings emphasize integrated PV-LED-T8 lighting solutions and optimized building envelope structures. Ranking analysis further highlights LED lighting, T8 high-efficiency fluorescent lamps, and rooftop PV systems as the top-recommended technologies for Beijing. Additionally, four policy recommendations are proposed to facilitate the large-scale implementation of the program. This study presents a holistic technical integration strategy that simultaneously enhances the technological performance, economic viability, and carbon reduction outcomes of architectural design and renovation. It also establishes a replicable decision-support framework for decarbonizing office and public buildings in cities, thereby supporting China’s “dual carbon” goals and contributing to global carbon mitigation efforts in the building sector. Full article