Search Results (331)

Understanding the carbon emission efficiency of apple production (APCEE) is critical for promoting green and low-carbon agricultural development. However, the spatiotemporal dynamics and driving factors of APCEE in China remain inadequately explored. This study employs life cycle assessment, super-efficiency slacks-based measures, and a panel Tobit model to evaluate the carbon footprint, APCEE, and its determinants in China’s two major production regions from 2003 to 2022. The results reveal that: (1) Producing one ton of apples in China results in 0.842 t CO₂e emissions. Land carbon intensity and total carbon emissions peaked in 2010 (28.69 t CO₂e/ha) and 2014 (6.52 × 10⁷ t CO₂e), respectively, exhibiting inverted U-shaped trends. Carbon emissions from various production areas show significant differences, with higher pressure on carbon emission reduction in the Loess Plateau region, especially in Gansu Province. (2) The APCEE in China exhibits a W-shaped trend (mean: 0.645), with overall low efficiency loss. The Bohai Bay region outperforms the Loess Plateau and national averages. (3) The structure of the apple industry, degree of agricultural mechanization, and green innovation positively influence APCEE, while the structure of apple cultivation, education level, and agricultural subsidies negatively impact it. Notably, green innovation and agricultural subsidies display lagged effects. Moreover, the drivers of APCEE differ significantly between the two major production regions. These findings provide actionable pathways for the green and low-carbon transformation of China’s apple industry, emphasizing the importance of spatially tailored green policies and technology-driven decarbonization strategies. 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

At a critical juncture in the global low-carbon transition, the role of artificial intelligence (AI) in facilitating low-carbon growth has become increasingly significant. To accelerate the integration of AI with socio-economic development, China has established National New Generation Artificial Intelligence Innovation and Development Pilot Zones (AIPZ). However, the specific impact of these zones on low-carbon development remains unclear. This study utilized panel data from 30 provinces in China from 2013 to 2022 and employed the multi-period difference-in-differences (DID) model and the spatial autoregressive difference-in-differences (SARDID) model to examine the carbon emissions reduction effects of the AIPZ policy and its spatial spillover effects. The findings revealed that the policy significantly reduced carbon emissions intensity (CEI) across provinces, with an average reduction effect of 6.9%. The analysis of the impact mechanism confirmed the key role of human, technological, and financial resources. Heterogeneity analysis indicated varying effects across regions, with more significant reductions in eastern and energy-rich areas. Further analysis using the SARDID model confirmed spatial spillover effects on CEI. This paper aims to enhance understanding of the relationship between AIPZ and CEI and provide empirical evidence for policymakers during the low-carbon transition. By exploring the potential of the AIPZ policy in emissions reduction, it proposes targeted strategies and implementation pathways for policymakers and industry participants to promote the sustainable development of China’s low-carbon economy. Full article

Commitments to carbon neutrality at both international and national levels have spurred the development of market-based mechanisms that incentivize low-carbon technologies while penalizing emissions-intensive activities. These policies have wide ranging impacts for the Australian agricultural sector, and associated rural communities, where the majority of carbon credits and biodiversity credits are sourced in Australia. Undeniably, the introduction of carbon and environmental markets has created the opportunity for an expansion and diversification of local, rural economies beyond a traditional agricultural base. However, there is much complexity for the agricultural sector to navigate as environmental markets intersect and compete with food and fiber livelihoods, and entrenched ideologies of rural identity and purpose. As carbon and environmental markets focused on primary producers have expanded rapidly, there is little understanding of the associated situated and relational impacts for farming households and rural communities. Nor has there been much work to identify the barriers to engagement. This study explores these tensions through qualitative research in Stanthorpe and Roma, Queensland, offering insights into the barriers and benefits of market engagement. The findings inform policy development aimed at balancing climate goals with agricultural sustainability and rural community resilience. Full article

China’s national strategies emphasize both achieving carbon peaking and neutrality (“dual carbon” objectives) and fostering high-quality economic development. This dual focus highlights the critical importance of the Green and Low-Carbon Transition (GLCT) of the economy and the development of New Quality Productive Forces (NQPF). Firms are central actors in this transformation, prompting the core research question: How does corporate engagement in GLCT contribute to the formation of NQPF? We investigate this relationship using panel data comprising 33,768 firm-year observations for A-share listed companies across diverse industries in China from 2012 to 2022. Corporate GLCT is measured via textual analysis of annual reports, while an NQPF index, incorporating both tangible and intangible dimensions, is constructed using the entropy method. Our empirical analysis relies primarily on fixed-effects regressions, supplemented by various robustness checks and alternative econometric specifications. The results demonstrate a significantly positive relationship: corporate GLCT robustly promotes the development of NQPF, with dynamic lag structures suggesting delayed productivity realization. Mechanism analysis reveals that this effect operates through three primary channels: improved access to financing, stimulated collaborative innovation and enhanced resource-allocation efficiency. Heterogeneity analysis indicates that the positive impact of GLCT on NQPF is more pronounced for state-owned enterprises (SOEs), firms operating in high-emission sectors, those in energy-efficient or environmentally friendly industries, technology-intensive sectors, non-heavily polluting industries and companies situated in China’s eastern regions. Overall, our findings suggest that corporate GLCT enhances NQPF by improving resource-utilization efficiency and fostering innovation, with these effects amplified by specific regional advantages and firm characteristics. This study offers implications for corporate strategy, highlighting how aligning GLCT initiatives with core business objectives can drive NQPF, and provides evidence relevant for policymakers aiming to optimize environmental governance and foster sustainable economic pathways. Full article

Addressing the sustainability challenges posed by the expanding livestock sector is crucial for China’s green transition. With the transformation of national dietary structure and increasing demand for livestock products, the associated resource consumption and environmental impacts, particularly carbon emissions have intensified. Reducing carbon emissions from livestock is vital for mitigating global warming, enhancing resource utilization efficiency, improving ecosystems and biodiversity, and ultimately achieving sustainable development of the livestock industry. Against this backdrop, this study measures the carbon emissions from livestock sector employing the Life Cycle Assessment (LCA) method, and applies the Generalized Divisia Index Method (GDIM) to analyze the factors affecting the changes in carbon emissions, aiming to quantify and analyze the carbon footprint of China’s livestock sector to inform sustainable practices. The findings reveal that China’s total carbon emissions from the livestock sector fluctuated between 645.15 million tons and 812.99 million tons from 2000 to 2023. Since 2020, emissions have entered a new phase of continuous growth, with a 5.40% increase in 2023 compared to 2020. Significantly, a positive trend toward sustainability is observed in the substantial decline of carbon emission intensity over the study period, with notable reductions in emission intensity across provinces and a gradual convergence in inter-provincial disparities. Understanding the drivers is key for effective mitigation. The output level and total mechanical power consumption level emerged as primary positive drivers of carbon emissions, while output carbon intensity and mechanical power consumption carbon intensity served as major negative drivers. Moving forward, to foster a sustainable and low-carbon livestock sector, China’s livestock sector development should prioritize coordinated carbon reduction across the entire industrial chain, adjust the industrial structure, and enhance the utilization efficiency of advanced low-carbon agricultural machinery while introducing such equipment. Full article

Mangrove forests serve as critical ecological barriers in coastal zones and play a vital role in global blue carbon sequestration strategies. In recent decades, China’s mangrove ecosystems have experienced complex interactions between degradation and restoration under intense coastal urbanization and systematic conservation efforts. However, the long-term spatiotemporal patterns and driving mechanisms of mangrove ecosystem health changes remain insufficiently quantified. This study developed a multi-temporal analytical framework using Landsat imagery (1986–2021) to derive kernel normalized difference vegetation index (kNDVI) time series—an advanced phenological indicator with enhanced sensitivity to vegetation dynamics. We systematically characterized mangrove growth patterns along China’s southeastern coast through integrated Theil–Sen slope estimation, Mann–Kendall trend analysis, and Hurst exponent forecasting. A Deep Forest regression model was subsequently applied to quantify the relative contributions of environmental drivers (mean annual sea surface temperature, precipitation, air temperature, tropical cyclone frequency, and relative sea-level rise rate) and anthropogenic pressures (nighttime light index). The results showed the following: (1) a nationally significant improvement in mangrove vitality (p < 0.05), with mean annual kNDVI increasing by 0.0072/yr during 1986–2021; (2) spatially divergent trajectories, with 58.68% of mangroves exhibiting significant improvement (p < 0.05), which was 2.89 times higher than the proportion of degraded areas (15.10%); (3) Hurst persistence analysis (H = 0.896) indicating that 74.97% of the mangrove regions were likely to maintain their growth trends, while 15.07% of the coastal zones faced potential degradation risks; and (4) Deep Forest regression id the relative rate of sea-level rise (importance = 0.91) and anthropogenic (nighttime light index, importance = 0.81) as dominant drivers, surpassing climatic factors. This study provides the first national-scale, 30 m resolution assessment of mangrove growth dynamics using kNDVI, offering a scientific basis for adaptive management and blue carbon strategies in subtropical coastal ecosystems. Full article

Accurate forecasting of carbon emissions at the county level is critical to support China’s dual-carbon goals. However, most current studies are limited to national or provincial scales, employing traditional statistical methods inadequate for capturing complex nonlinear interactions and spatiotemporal dynamics at finer resolutions. To overcome these limitations, this study develops and validates a high-resolution predictive model using advanced gradient boosting algorithms—Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM)—based on socioeconomic, industrial, and environmental data from 2732 Chinese counties during 2008–2017. Key variables were selected through correlation analysis, missing values were interpolated using K-means clustering, and model parameters were systematically optimized via grid search and cross-validation. Among the algorithms tested, LightGBM achieved the best performance (R² = 0.992, RMSE = 0.297), demonstrating both robustness and efficiency. Spatial–temporal analyses revealed that while national emissions are slowing, the eastern region is approaching stabilization, whereas emissions in central and western regions are projected to continue rising through 2027. Furthermore, SHapley Additive exPlanations (SHAP) were applied to interpret the marginal and interaction effects of key variables. The results indicate that GDP, energy intensity, and nighttime lights exert the greatest influence on model predictions, while ecological indicators such as NDVI exhibit negative associations. SHAP dependence plots further reveal nonlinear relationships and regional heterogeneity among factors. The key innovation of this study lies in constructing a scalable and interpretable county-level carbon emissions model that integrates gradient boosting with SHAP-based variable attribution, overcoming limitations in spatial resolution and model transparency. Full article

This paper examines the impact of carbon capture and storage (CCS) deployment on national carbon intensity (CI) across 43 countries from 2010 to 2020. Using a dynamic common correlated effects (DCCE) log–log panel, we estimate the elasticity of CI with respect to sectoral CCS facility counts within four income-group panels and the full sample. In the high-income panel, CCS in direct air capture, cement, iron and steel, power and heat, and natural gas processing sectors produces statistically significant CI declines of 0.15%, 0.13%, 0.095%, 0.092%, and 0.087% per 1% increase in facilities, respectively (all p < 0.05). Upper-middle-income countries exhibit strong CI reductions in direct air capture (–0.22%) and cement (–0.21%) but mixed results in other sectors. Lower-middle- and low-income panels show attenuated or positive elasticities—reflecting early-stage CCS adoption and infrastructure barriers. Robustness checks confirm these patterns both before and after the 2015 Paris Agreement and between emerging and developed economy panels. Spatial analysis reveals that the United States and United Kingdom achieved 30–40% CI reductions over the decade, whereas China, India, and Indonesia realized only 10–20% declines (relative to a 2010 baseline), highlighting regional deployment gaps. Drawing on these detailed income-group insights, we propose tailored policy pathways: in high-income settings, expand tax credits and public–private infrastructure partnerships; in upper-middle-income regions, utilize blended finance and technology-transfer programs; and in lower-income contexts, establish pilot CCS hubs with international support and shared storage networks. We further recommend measures to manage CCS’s energy and water penalties, implement rigorous monitoring to mitigate leakage risks, and design risk-sharing contracts to address economic uncertainties. Full article

Facilitating industrial restructuring and modernization plays a pivotal role in realizing China’s dual-carbon objectives (carbon peaking and carbon neutrality) and advancing sustainable socioeconomic progress. Leveraging panel data from 30 provincial-level administrative units (2005–2022) and adopting the Spatial Durbin Model, this research investigates how industrial structure upgrading influences carbon emission intensity within the framework of a unified national market, while elucidating its operational mechanisms. The key findings include the following: (1) Provincial carbon emission intensity demonstrates pronounced “high-high” and “low-low” spatial agglomeration during the study period. Industrial restructuring exhibits marked carbon abatement effects, accompanied by discernible cross-regional spillover benefits. (2) Industrial structure upgrading can reduce carbon emission levels by promoting the technology diffusion effect, while the competitive demonstration effect of digitalization has not yet manifested. (3) The establishment of an integrated national market enhances the capacity of industrial upgrading to suppress carbon emission intensity. (4) The emission-reducing impacts of industrial restructuring manifest heterogeneous patterns across regions and temporal phases: In Eastern China, industrial upgrading paradoxically elevates emission intensity. Central-western regions experience significant emission reductions. Temporally, the relationship follows an inverted U-shaped trajectory. These insights underscore the necessity for policymakers to refine industrial modernization strategies, expedite nationwide market integration mechanisms, and cultivate region-specific green transition roadmaps. Full article

Under the dual constraints of rigid water resource management systems and China’s “dual carbon” national strategy, water resource management authorities face pressing practical demands for the coordinated governance of water conservation and carbon dioxide emission reduction. This study comprehensively compiles nationwide data on water supply/consumption, energy use, water intensity, and CO₂ emissions across Chinese provinces. Employing a non-radial directional distance function (NDDF) model with multiple inputs and outputs, we quantitatively assess provincial water saving and carbon reduction performance during 2000–2021; measure synergistic effects; and systematically examine the spatiotemporal evolution, correlation patterns, and convergence trends of three key indicators: standalone water saving performance, standalone carbon reduction performance, and their synergistic performance—essentially addressing whether “1 + 1 > 2” holds true. Furthermore, we analyze the spatial convergence and clustering characteristics of synergistic effect across regions, delving into the underlying causes of inter-regional disparities in water–carbon synergy. Key findings reveal the following: ① Temporally, standalone water saving and carbon reduction performance generally improved, though the water saving metrics initially declined before stabilizing into sustained growth, ultimately outpacing carbon reduction gains. Synergistic performance consistently surpassed standalone measures, with most regions demonstrating accelerating synergistic enhancement over time. Nationally, water–carbon synergy exhibited early volatile declines followed by steady growth, though the growth rate gradually decelerated. ② Spatially, high-value synergy clusters migrated from the western to eastern regions and the northern to southern zones before stabilizing geographically. The synergy effect demonstrates measurable convergence overall, yet with pronounced regional heterogeneity, manifesting a distinct “high southeast–low northwest” agglomeration pattern. Strategic interventions should prioritize water–carbon nexus domains, leverage spatial convergence trends and clustering intensities, and systematically unlock synergistic potential. Full article

This study investigates how various renewable energy technologies influence national carbon intensity (CO₂ emissions per unit of GDP) across 184 countries over the period 2000–2020. In the context of Sustainable Development Goals (SDG 7 and SDG 13) and the post-Paris-Agreement policy landscape, it addresses the gap in understanding technology-specific decarbonization effects and the role of governance. A dynamic panel framework employing the Dynamic Common Correlated Effects (DCCE) estimator accounts for cross-sectional dependence and temporal persistence, while disaggregating total renewables into hydropower, wind, solar, and geothermal generation. Environmental regulation is incorporated as a moderating variable using the World Bank’s Regulatory Quality index. Empirical results demonstrate that higher renewable generation is associated with statistically significant reductions in carbon intensity, with hydropower showing the most consistent negative effect across all income groups. Solar and geothermal technologies yield substantial carbon-reducing impacts in lower-middle-income settings once supportive policies are in place. Wind exhibits heterogeneous outcomes: positive or insignificant effects in some high- and upper-middle-income panels prior to 2015, shifting toward neutral or negative after more stringent regulation. Interaction terms reveal that stronger regulatory environments amplify renewable-driven decarbonization, particularly for intermittent sources such as wind and solar. Key contributions include (1) a comprehensive global assessment of four disaggregated renewable technologies; (2) integration of regulatory quality into decarbonization pathways, illustrating post-2015 policy moderations; and (3) methodological advancement through a large-sample DCCE approach that captures unobserved common shocks and heterogeneous country dynamics. These findings inform targeted policy measures—such as prioritizing hydropower where feasible, strengthening regulatory frameworks, and tailoring technology strategies—to accelerate low-carbon energy transitions worldwide. Full article

Cotton is the most widely consumed natural fiber globally and emits fewer greenhouse gases compared to synthetic alternatives. Brazil is currently the largest cotton exporter, and understanding its potential for sustainable expansion is crucial. This study developed agroclimatic zoning maps for cotton (Gossypium hirsutum L.) across Brazil under current and future climate conditions using data from the World-Clim and MapBiomas platforms. Four climate change scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) were assessed over multiple time periods. Results showed that rising temperatures and reduced rainfall will likely reduce cotton suitability in traditional producing regions such as Bahia. However, areas with potential for cotton cultivation, especially in Mato Grosso, which currently accounts for 90% of national production, remain extensive, with agroclimatic conditions indicating a theoretical expansion potential of up to 40 times the current cultivated area. This projection must be interpreted with caution, as it does not account for economic, logistical, or social constraints. Notably, Brazilian cotton is cultivated with minimal irrigation, low fertilizer input, and high adoption of no-till systems, making it one of the least carbon-intensive globally. Full article

The low-carbon economy is becoming a critical global development paradigm. As the world’s largest carbon emitter, China’s transition toward low-carbon practices in its construction sector is pivotal for achieving its carbon peaking and carbon neutrality goals. Research into the decarbonization pathways and driving factors of this energy- and emission-intensive industry is essential. It not only reduces the sector’s dependence on traditional energy sources but also provides vital support for China’s national energy conservation and emissions reduction strategy. As the construction industry transitions toward low-carbon sustainability, traditional unidimensional assessments based solely on socio-economic and ecological factors are inadequate. This study proposed an integrated evaluation framework using the CRITIC–TOPSIS model, incorporating technological, social, economic, industrial, and energy dimensions. Panel data on energy consumption in the Yangtze River Delta (YRD) region were employed to assess the construction sector’s low-carbon development level and an ARIMA model was utilized to forecast its low-carbon potential. The results indicate that from 2011 to 2022, the sector’s total carbon emissions followed a unimodal trajectory (initial increase followed by decline), with indirect emissions exceeding 90%, primarily from cement, steel, and other building materials. The regional construction industry exhibited a unimodal trajectory in low-carbon development, characterized by an initial increase followed by a decline. Average construction carbon emissions reached 41,637.5877 million tons, with a transient surge (69.67% increase) occurring between 2011 and 2014. This was followed by a 41.83% reduction from 2014 to 2022, with emissions projected to stabilize and gradually increase through 2030. Technological and industrial factors constitute the primary drivers of sectoral low carbon. Quantitative analysis identified the capital utilization rate, industrial structure, and construction industry gross domestic product (GDP) as key impediments to low-carbon transition, with average impedance degrees of 8.713%, 12.280%, and 12.697%, respectively. This study has revealed the key driving factors for the low-carbon development of the construction industry, extending theoretical frameworks for construction industry sustainability. These findings offer empirical support for formulating regionally differentiated carbon mitigation policies. Full article

This study explores the relationship between firm-level carbon productivity (CRP), home country environmental performance (HCEP), and firm performance—both financial and international—in the global meat exporting industry. While prior research has examined these dynamics in manufacturing sectors, limited attention has been paid to the meat industry, which is both economically significant and environmentally intensive. Using a multiple case study approach, we analyze data from three leading meat-exporting firms—Agrosuper (Chile), BRF (Brazil), and Danish Crown (Denmark)—over the period 2020–2023. CRP is operationalized as the ratio of firm output to CO₂ emissions, while HCEP is measured by national emissions per million USD of GDP. Financial performance is assessed via return on assets (ROA), and international performance through export intensity. Our findings reveal a nuanced relationship between CRP and firm performance. Contrary to theoretical expectations, a higher CRP does not consistently translate into improved financial performance, suggesting potential trade-offs between sustainability investments and profitability. However, a positive association is observed between CRP and international performance, particularly in firms operating within environmentally advanced countries. These results highlight the importance of home country environmental contexts in shaping firms’ global competitiveness. This research contributes to the literature by introducing CRP as a firm-level metric in the meat industry and by emphasizing the moderating role of HCEP. The findings offer practical implications for policymakers and managers seeking to align environmental responsibility with economic and international performance goals. Full article