Search Results (79)

This research evaluates the carbon dioxide emission-reduction efficiency of new energy vehicles (NEVs) in China from 2018 to 2023 by applying a three-stage super-SBM data envelopment analysis (DEA) model that incorporates undesirable outputs. This model offers significant advantages over traditional DEA models, as it effectively disentangles the influences of external environmental factors and stochastic noise, thereby providing a more accurate and robust assessment of true efficiency. Its super-efficiency characteristic also allows for effective ranking of all decision-making units (DMUs) on the efficiency frontier. The empirical findings reveal several key insights. (1) The NEV industry’s carbon-reduction efficiency in China between 2018 and 2023 displayed an upward trend accompanied by pronounced fluctuations. Its mean super-efficiency score was 0.353, indicating substantial scope for improvements in scale efficiency. (2) Significant interprovincial disparities in efficiency appear. Unbalanced coordination between production and consumption in provinces such as Shaanxi, Beijing, and Liaoning has produced correspondingly high or low efficiency values. (3) Although accelerated urbanization has reduced the capital and labor inputs required by the NEV industry and has raised energy consumption, the net effect enhances carbon-reduction efficiency. Household consumption levels and technological advancement exerts divergent effects on efficiency. The former negatively relates to efficiency, whereas the latter is positively associated. Full article

To support the national goals of carbon peaking and carbon neutrality, this study proposes a household carbon emission prediction model based on Gene Expression Programming (GEP) for low-carbon retrofitting of aging residential areas in arid-cold regions. Focusing on 15 typical aging communities in Kundulun District, Baotou City, a 17-dimensional dataset encompassing building characteristics, demographic structure, and energy consumption patterns was collected through field surveys. Key influencing factors (e.g., electricity usage and heating energy consumption) were selected using Pearson correlation analysis and the Random Forest (RF) algorithm. Subsequently, a hybrid prediction model was constructed, with its parameters optimized by minimizing the root mean square error (RMSE) as the fitness function. Experimental results demonstrated that the model achieved an R² value of 0.81, reducing RMSE by 77.1% compared to conventional GEP models and by 60.4% compared to BP neural networks, while significantly improving stability. By combining data dimensionality reduction with adaptive evolutionary algorithms, this model overcomes the limitations of traditional methods in capturing complex nonlinear relationships. It provides a reliable tool for precision-based low-carbon retrofits in aging residential areas of arid-cold regions and offers a methodological advance for research on building carbon emission prediction driven by urban renewal. Full article

Japan’s shift to a super-aged society is reshaping household carbon footprint (HCF) in ways that vary by age, income, and region. Drawing on a two-tier national–prefectural framework, we quantify the influence of demographic shifts on HCF and evaluate inequalities, and project prefectural HCF to 2050 under fixed 2005 technology and consumption baselines. Nationally, emissions follow an inverted-U age curve, peaking at the 50–54 s (2.16 tCO₂) and dropping at both the younger and older ends. Carbon inequality—the gap between high- and low-income households—displays the opposite U shape, being the widest below 30 and above 85. Regional HCF patterns add a further layer: while the inverted U persists, its peak shifts to the 60–64 s in high-income prefectures such as Tokyo—where senior emissions rise by 44% by 2050—and to the 45–49 s in low-income prefectures such as Akita, where younger age groups cut emissions by 58%. Although spatial carbon inequality narrows through midlife, it widens again in old age as eldercare and home energy needs grow. These findings suggest that a uniform mitigation trajectory overlooks key cohorts and regions. To meet the 2050 net-zero target, Japan should integrate age-, income-, and region-specific interventions—for example, targeted carbon pricing, green finance for middle-aged consumers, and less-urban low-carbon eldercare—into its decarbonization roadmap. Full article

The choice of carbon inequality metrics can significantly influence demand-side mitigation policies and their equity outcomes. We propose integrated carbon inequality metrics, including juxtaposing carbon inequality with economic inequality, disparity ratios across income and age groups, and structural income–urbanization inequality patterns. We then apply these new metrics and use the household expenditure survey data from China Family Panel Studies as a case study to examine household consumption-based carbon emissions in China. We assess the extent to which household consumption patterns, household expenditure, age, and urbanization contribute to the gap in per-capita household carbon footprints (CF) across income groups. We find that in relative terms, the top 20% income group accounts for 38% of total emissions, whereas the bottom 20% emit about 8% in China. Per-capita CFs vary slightly widely in their inequality than expenditure. The CF disparity ratios of all eight consumption categories across provinces concentrate around 4.5. CF disparity ratios of households with elderly members range from 1 to 3 and decrease with increasing household size. Rural CF-Gini exhibit a slightly wider range (0.15 to 0.52) than urban CF-Gini (0.16 to 0.42). Per capita CF of urban inhabitants was substantially larger than that of the rural ones, with 8.83 tCO₂ per capita in urban regions vs. 2.68 tCO₂ in rural regions. This study provides a nuanced understanding of within-country disparities to inform equitable demand-side mitigation solutions. Full article

This study explores the driving factors of household energy consumption in high-density residential areas of Beijing and proposes targeted energy-saving strategies. Data were collected through field surveys, questionnaires, and interviews, covering 16 influencing factors across household, building, environment, and transportation categories. A hyperparameter-optimized ensemble model (XGBoost, RF, GBDT) was employed, with XGBoost combined with genetic algorithm tuning performing best. SHAP analysis revealed that key factors varied by season but included floor level, daily travel distance, building age, greening rate, water bodies, and household age. The findings inform strategies such as optimizing workplace–residence layout, improving building insulation, increasing green spaces, and promoting community energy-saving programs. This study provides refined data support for energy management in high-density residential areas, enhances the application of energy-saving technologies, and encourages low-carbon lifestyles. By effectively reducing energy consumption and carbon emissions during the operational phase of residential areas, it contributes to urban green development and China’s “dual carbon” goals. Full article

How to promote the household energy transition (HET) has become an important response to extreme climate change. Our paper examines whether a low-carbon city pilot (LCCP) can promote HET. We empirically use the Staggered Difference-in-Differences (DID) model to explore its mechanisms. The results indicate that the LCCP can substantially promote HET. The primary driving mechanism underlying this transition is enhanced governmental emphasis on carbon emission reduction and elevated public environmental awareness. However, the increased local expenditure on energy conservation and environmental protection does not serve as an effective mechanism. The heterogeneity analysis reveals that the LCCP has the most pronounced impact on HET among high-income groups, whereas the effect on low-income groups is relatively minor. Furthermore, the LCCP significantly promotes HET in the eastern region and urban areas, while the central region tends to inhibit it, and the western region and rural areas show no significant effect. The heterogeneity analysis further reveals that the LCCP is effective in Municipalities and Strong-Capital Provinces, where centralized governance and strong political incentives enhance policy implementation. In contrast, the policy shows limited or even negative effects in Non-Municipal Provinces and Non-Strong-Capital Provinces. We provide valuable policy insights for governments to bolster the LCCP implementation to promote HET and achieve carbon neutrality at an earlier stage. Full article

Household energy consumption is one of the major drivers of carbon emissions, and an in-depth analysis of its influencing factors, along with forecasting carbon emission trajectories, is crucial for achieving China’s carbon emission targets. This study reviews the research progress on urban household energy-related carbon emissions (HErC) in China since 2000, with a focus on the latest developments in influencing factors. The study categorizes these factors into five major groups: household characteristics, economic attributes, energy consumption features, awareness and norms, and policies and interventions. The findings indicate that income levels, energy efficiency, and household size are the key determinants of urban HErC of China and are commonly used as core assumptions in scenario-based forecasts of emission trends. In addition, although environmental awareness and government services have increasingly garnered attention, their specific effects require further investigation due to the challenges in quantification. A synthesis of existing forecasting studies suggests that, without the implementation of effective measures, HErC will continue to rise, and the peak emission period will be delayed. Enhancing building and energy efficiency, promoting low-carbon consumption and clean energy applications, and implementing multidimensional coordinated policies are considered the most effective pathways for emission reduction. Full article

An important aspect of a well-designed urban form is supporting active school travel by adolescents, as it has positive effects on physical activity, healthy lifestyles, and reducing vehicle-related carbon emissions. To achieve this, it is necessary to provide sufficient shading and fewer detours on home–school routes, especially in an era of frequent heatwaves. Analyzing the school travel environment at the city scale is essential for identifying practical solutions and informing comprehensive urban policy-making. This study proposes a framework for investigating, assessing, and intervening in home–school routes in Nanjing, China, emphasizing a dual assessment of commuting routes based on the pedestrian detour ratio and shading ratio. This work reveals that approximately 34% of middle school households in Nanjing face challenges in walking to and from school, with only 24.18% of walking routes offering fewer detours and sufficient shade. We advocate reengineering urban forms by reducing barriers to facilitate shortcuts, thereby providing school-age students with better access to cooler and healthier environments, aiming to promote walking and reduce car dependence. The findings may encourage more families to engage in active commuting and serve as a lever to drive school decarbonization and combat climate warming. Our work, with transferability to other cities, can assist urban designers in piloting urban (re)form incrementally and pragmatically to promote sustainable urban agendas. Full article

Households account for 20–40% of carbon emissions in urban areas, making them critical to achieving urban sustainability. Integrating smart technologies in households offers a promising pathway to enhance energy efficiency, mitigate climate change, and support the transition from Smart Cities to Sustainable Smart Cities (SSCs). However, achieving this transition requires not only technological adoption but also behavioural shifts that influence energy consumption—a gap in existing studies. This study examines how household engagement with smart technologies impacts behavioural change and systemic sustainability transitions. Using the Multi-Level Perspective (MLP) framework enriched with System Thinking through Causal Loop Diagrams (CLDs), qualitative data were collected via 11 household interviews exhibiting varying engagement levels. The findings revealed three household-regime dynamics: proactive households driving systemic change through innovation, moderately engaged households contributing to regime stability with financial incentives fostering gradual adoption, and resistant households reinforcing existing structures due to privacy concerns. By extending the MLP framework to incorporate behavioural and social dimensions, the study provided insights into how micro-level behaviours influence macro-level transitions, challenging techno-centric narratives. The findings underscore the need for policies that enhance awareness, address privacy concerns, and provide tailored incentives to catalyse smart technology adoption and energy efficiency, fostering a more inclusive and effective pathway toward sustainable urban futures. Full article

The complex interplay between digital finance (DF) and household carbon emissions (HCEs) represents a critical subsystem within the broader socioeconomic–ecological system driving climate change. This paper presents estimates of HCEs based on panel data for 30 Chinese provinces from 2011 to 2021 and examines the effects and mechanisms of DF on HCEs in urban and rural regions. The results indicate that (1) DF has a negative impact on urban HCEs, while, conversely, it exacerbates HCEs in rural regions; (2) based on the heterogeneity analysis, the impact of DF is primarily driven by its coverage, with the most significant effects seen in eastern China; and (3) two transmission channels are operative: an energy consumption scale effect and an energy consumption composition effect. Further analysis suggests that government expenditure on energy conservation and environmental protection, as well as financial regulation, play moderating roles in these channels. These findings provide new insights into efforts to achieve carbon neutrality in China and offer new perspectives on the role of financial technologies in shaping environmental outcomes within complex socio-technical systems. Full article

The enhancement of the carbon trading mechanism signifies a gradual transition in China’s environmental regulatory framework, shifting from a command and control approach to a market-based incentive model. Despite the significance of this shift, existing research has insufficiently explored the impact of market-oriented environmental policies on consumption-based emissions. This study leverages the carbon trading policies implemented in 2013 as a quasi-natural experiment, combined with a precise measurement of urban and rural household carbon emissions (HCE) during 2005–2021. Employing a difference-in-differences method, we evaluate the heterogeneous impact of these policies on urban and rural HCE. The results demonstrate a significantly negative effect of the policies on indirect HCE, a conclusion that remains robust across various placebo and robustness tests. Furthermore, we identify the transmission mechanisms through which carbon trading policies affect the reduction in HCE. The results indicate that the policy has a significant negative impact on indirect HCE, with a notable urban–rural difference. The effect of the policy is −0.829 for urban areas and −0.365 for rural areas, a conclusion that remains robust across various placebo and robustness checks. Additionally, we identified two transmission mechanisms through which carbon trading policies operate: financial deepening and employment effects. Lastly, we found that carbon trading policies can reduce carbon inequality between urban and rural areas by 46.8%. Full article

Despite the efforts to examine the influence of urban forms on CO₂ emissions, most studies have mainly measured urban forms from a two-dimensional perspective, with relatively little attention given to three-dimensional urban forms and their causal relationships. Utilizing the built-up area dataset from the Global Human Settlement Layer (GHSL) project and the carbon emission dataset from the China City Greenhouse Gas Working Group (CCG), we examine a causal and heterogeneous effect of three-dimensional urban forms on CO₂ emissions—specifically urban height, density, and intensity—in 285 Chinese cities. The empirical results reveal a robust and positive causal effect of 3D urban forms on carbon emissions. Even when incorporating the spatial spillover effect, the positive effect of 3D urban forms remains. Moreover, GDP per capita and total population have a greater impact on urban CO₂ emissions. Additionally, we find that the influence of 3D urban forms on CO₂ emissions is U-shaped, with total population serving as a moderating factor in this effect. Importantly, there is significant geographic and sectoral heterogeneity in the influence of 3D urban forms on CO₂ emissions. Specifically, the influence of 3D urban forms is greater in eastern cities than in non-eastern cities. Furthermore, 3D urban forms primarily influence household carbon emissions rather than industrial and transportation carbon emissions. Therefore, in response to the growing challenges of global climate change and environmental issues, urban governments should adopt various strategies to develop more rational three-dimensional urban forms to reduce CO₂ emissions. Full article

The increasing carbon dioxide emissions from human activities present a significant global concern, with approximately two-thirds of greenhouse gas emissions attributed to household activities. The Carbon Generalized System of Preferences (CGSP) has emerged as a pivotal mechanism to incentivize voluntary carbon reduction in community households. This paper examines the development of a community digital management platform designed to incentivize voluntary carbon reduction at the community level, highlighting the critical role of reducing emissions in urban community life to meet carbon peak and neutrality targets. This study employs the TAO model of Three-Way Decision to establish a closed-loop operational framework for the CGSP digital platform. The platform features a Trisection mechanism to record and quantify low-carbon behaviors, an Action mechanism to classify and reward community members, and an Outcome mechanism to assess overall community carbon reduction achievements. Additionally, a user interface tailored for community users is developed to enhance platform accessibility. The proposed platform presents a practical and innovative solution for exploring emission reduction potential in urban communities. By systematically recording low-carbon behaviors, providing targeted rewards, and conducting comprehensive assessments, the platform aims to guide community residents in adopting sustainable practices. This study offers a valuable reference for the digital transformation, intelligent system construction, and development of new urban functional units within communities. Full article

The COVID-19 pandemic has led to a surge in face mask demand, resulting in increased face mask waste and environmental impacts. This study investigates mask usage patterns and the environmental impacts of single-use and cloth masks across three phases: pre-COVID-19, COVID-19, and the new normal. A comprehensive survey conducted in five cities across four Asian countries reveals a surge in mask usage during COVID-19 (6.81 pieces/week), followed by a decline in the new normal (3.73 pieces/week), though usage remained higher than pre-COVID-19 levels (1.46 pieces/week). For single-use masks, age significantly impacts usage in all cities, while gender and education level affect usage in Shanghai, Harbin, and Depok. Household income influences mask use in Shanghai and Harbin. For cloth masks, education level significantly correlates with usage in most cities. The study highlights the significant environmental impact of mask use, particularly in densely populated urban areas. Switching to cloth masks for one year could reduce carbon footprints by 44.27–81.9 million kgCO₂eq, decrease solid waste by 34.81–52.41 million kg, and reduce microplastic emissions by 6.50 to 15.56 trillion particles in the first 24 h after disposal. However, this transition may increase water usage by 1.73–1.86 billion m³H₂Oeq. The study also offers policy recommendations on mask usage and disposal. Full article

Incorporating Internet of Things (IoT) technology into indoor kale cultivation holds significant promise for revolutionizing organic farming methodologies. While numerous studies have investigated the impact of environmental factors on kale growth in IoT-based smart agricultural systems, such as temperature, humidity, and nutrient levels, indoor ultraviolet (UV) LED light’s operational efficiencies and advantages in organic farming still need to be explored. This study assessed the efficacy of 15 UV light-controlling indoor experiments in three distinct lighting groups: kale cultivated using conventional household LED lights, kale cultivated using specialized indoor UV lights designed for plant cultivation, and kale cultivated using hybrid household and LED grow lights. The real-time IoT-based monitoring of light, soil, humidity, and air conditions, as well as automated irrigation using a water droplet system, was employed throughout the experiment. The experimental setup for air conditioning maintained temperatures at a constant 26 degrees Celsius over the 45-day study period. The results revealed that a combination of daylight household lights and indoor 4000 K grow lights scored the highest, indicating optimal growth conditions. The second group exposed to warm white household and indoor grow red light exhibited slightly lower scores but larger leaf size than the third group grown under indoor grow red light, likely attributable to reduced light intensity or suboptimal nutrient levels. This study highlights the potential of indoor UV LED light farming to address challenges posed by urbanization and climate change, thereby contributing to efforts to mitigate agricultural carbon emissions and enhance food security in urban environments. This research contributes to positioning kale as a sustainable organic superfood by optimizing kale cultivation. Full article