Search Results (325)

Hydrogen offers an effective pathway for the large-scale storage of renewable energy. For a tourist city located in a plateau region rich in renewable energy, hydrogen shows great potential for reducing carbon emissions and utilizing uncertain renewable energy. Herein, the wind–solar–hydrogen stand-alone and grid-connected systems in the plateau tourist city of Lijiang City in Yunnan Province are modeled and techno-economically evaluated by using the HOMER Pro software (version 3.14.2) with the multi-criteria decision analysis models. The system is composed of 5588 kW solar photovoltaic panels, an 800 kW wind turbine, a 1600 kW electrolyzer, a 421 kWh battery, and a 50 kW fuel cell. In addition to meeting the power requirements for system operation, the system has the capacity to provide daily electricity for 200 households in a neighborhood and supply 240 kg of hydrogen per day to local hydrogen-fueled buses. The stand-alone system can produce 10.15 × 10⁶ kWh of electricity and 93.44 t of hydrogen per year, with an NPC of USD 8.15 million, an LCOE of USD 0.43/kWh, and an LCOH of USD 5.26/kg. The grid-connected system can generate 10.10 × 10⁶ kWh of electricity and 103.01 ton of hydrogen annually. Its NPC is USD 7.34 million, its LCOE is USD 0.11/kWh, and its LCOH is USD 3.42/kg. This study provides a new solution for optimizing the configuration of hybrid renewable energy systems, which will develop the hydrogen economy and create low-carbon-emission energy systems. 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

Remanufacturing design is a green design model that considers remanufacturability during the design process to improve the reuse of components. However, traditional remanufacturing design scheme decision making focuses on the remanufacturability indicator and does not fully consider the carbon emissions of the remanufacturing process, which will take away the energy-saving and emission reduction benefits of remanufacturing. In addition, remanufacturing design schemes rarely consider the human ergonomics of the product, which leads to uncomfortable handling of the product by the customer. To reduce the remanufacturing carbon emission and improve customer comfort, it is necessary to select a reasonable design scheme to satisfy the carbon emission reduction and ergonomics demand; therefore, this paper proposes an integrated multi-criteria decision-making method for remanufacturing design that considers the carbon emission and human ergonomics. Firstly, an evaluation system of remanufacturing design schemes is constructed to consider the remanufacturability, cost, carbon emission, and human ergonomics of the product, and the evaluation indicators are quantified by the normalization method and the Kansei engineering (KE) method; meanwhile, the hierarchical analysis method (AHP) and entropy weight method (EW) are used for the calculation of the subjective and objective weights. Then, a multi-attribute decision-making method based on the combination of an assignment approximation of ideal solution ranking (TOPSIS) and gray correlation analysis (GRA) is proposed to complete the design scheme selection. Finally, the feasibility of the scheme is verified by taking a household coffee machine as an example. This method has been implemented as an application using Visual Studio 2022 and Microsoft SQL Server 2022. The research results indicate that this decision-making method can quickly and accurately generate reasonable remanufacturing design schemes. Full article

Household appliances constitute the second largest source of residential energy consumption in China, accounting for over 20% of the total and exhibiting a steady growth trend. Despite their substantial impact on energy demand and carbon emissions, a comprehensive analysis of the current status and future trends of household appliances in China is still lacking. This study employs the Long-Range Energy Alternatives Planning (LEAP) system to model energy consumption and carbon emissions for five major household appliances (air conditioners, refrigerators, washing machines, TVs, and water heaters) from 2022 to 2052. Three scenarios were analyzed: a Reference (REF) scenario (current trends), an Existing Policy Option (EPO) scenario (current energy-saving measures), and a Further Strengthening (FUR) scenario (enhanced efficiency measures). Key results show that by 2052, the EPO scenario achieves cumulative savings of 1074.8 billion kWh and reduces emissions by 580.7 million metric tons of CO₂ equivalent compared to REF. The FUR scenario yields substantially greater benefits, demonstrating the significant potential of strengthened policies. This analysis underscores the critical role of improving appliance energy efficiency and provides vital insights for policymakers and stakeholders aiming to reduce residential sector emissions. Full article

With the increasing integration of renewable energy and smart technologies in residential energy systems, proactive household energy management (HEM) have become critical for reducing costs, enhancing grid stability, and achieving sustainability goals. This study proposes a ultra-short-term forecasting-driven proactive energy consumption optimization strategy that integrates advanced forecasting models with multi-objective scheduling algorithms. By leveraging deep learning techniques like Graph Attention Network (GAT) architectures, the system predicts ultra-short-term household load profiles with high accuracy, addressing the volatility of residential energy use. Then, based on the predicted data, a comprehensive consideration of electricity costs, user comfort, carbon emission pricing, and grid load balance indicators is undertaken. This study proposes an enhanced mixed-integer optimization algorithm to collaboratively optimize multiple objective functions, thereby refining appliance scheduling, energy storage utilization, and grid interaction. Case studies demonstrate that integrating photovoltaic (PV) power generation forecasting and load forecasting models into a home energy management system, and adjusting the original power usage schedule based on predicted PV output and water heater demand, can effectively reduce electricity costs and carbon emissions without compromising user engagement in optimization. This approach helps promote energy-saving and low-carbon electricity consumption habits among users. Full article

Rural areas around the world are increasingly dealing with energy and environmental challenges. These challenges are particularly acute in developing countries, where persistent reliance on traditional energy sources—such as wood fuel—intersects with concerns about forest conservation and energy sustainability. While wood fuel use is often portrayed as unsustainable, it is important to acknowledge that much of it remains ecologically viable and socially embedded. This study explores the role of rural entrepreneurs in shaping low-carbon transitions at the intersection of household energy practices and environmental stewardship. Fieldwork was carried out in four rural Zambian communities in 2016 and complemented by 2024 follow-up reports. It examines the connections between household energy choices, greenhouse gas emissions, and forest resource dynamics. Findings reveal that over 60% of rural households rely on charcoal for cooking, with associated emissions estimated between 80 and 150 kg CO₂ per household per month. Although this is significantly lower than the average per capita carbon footprint in industrialized countries, such emissions are primarily biogenic in nature. While rural communities contribute minimally to global climate change, their practices have significant local environmental consequences. This study draws attention to the structural constraints as well as emerging opportunities within Zambia’s rural energy economy. It positions rural entrepreneurs not merely as policy recipients but as active agents of innovation, environmental monitoring, and participatory resource governance. A model is proposed to support sustainable rural energy transitions by aligning forest management with context-sensitive emissions strategies. 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

This study explores how carbon pricing and electricity market liberalization jointly contribute to China’s sustainable energy transition. Using a dynamic computable general equilibrium (CGE) model (CEEEA2.0), we simulate three policy scenarios—business as usual, emissions trading scheme (ETS) with regulated electricity prices, and ETS with market-based pricing—under a unified emissions cap. The results demonstrate that electricity market liberalization enhances carbon pricing efficiency by eliminating price distortions, leading to a 0.06% increase in GDP and a 12% reduction in emission abatement costs. However, liberalization also raises electricity and consumer prices, disproportionately affecting rural and low-income households. These findings underscore the need to balance economic efficiency and social equity in sustainability-oriented energy reforms. Our analysis emphasizes the importance of designing inclusive and just transition policies to ensure that carbon mitigation efforts support long-term environmental, economic, and social sustainability goals. 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

Air-pollution-related issues, including the rise in carbon dioxide emissions, require, among others, solutions that include using electric vehicles supplied by the energy obtained from renewable sources. These solutions also include the infrastructure for electric vehicle charging. However, the existing systems mostly employ independent subsystems (such as subsystems for the control of electric vehicle chargers, subsystems for the control of smart battery storage, etc.), leading to hardware redundancy, software complexity, increased hardware costs, and communication link complexity. An architecture of a system for remotely controlling a renewable-energy-source-powered sustainable electric vehicle charging station, which overcomes these deficiencies, is presented in this paper. Consideration is also given to the sizes and combinations of different parts (renewable sources, batteries, chargers, etc.) for various purposes (households, replacing current gas stations, big parking spaces in shopping centers, public garages, etc.). The ability to integrate a wide range of features into one system helps to optimize the use of several subsystems, including the ones that control electric vehicle chargers remotely, smart storage battery remote control, smart electricity meter remote control, and fiscal cash register remote control, creating a sustainable and economically efficient solution. In this manner, consumers of electric vehicles will have easier access to renewable-energy-powered sustainable charging stations. This helps to reduce the amount of air pollution and its harmful effects, including climate change, by promoting the use of electric vehicles that are powered by renewable energy sources. The energy independence and sustainability of the station were considered in such a way that the owner of the station achieves maximum economic benefits. 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

Understanding how household carbon emissions vary across time and regions is essential for promoting low-carbon lifestyles and advancing sustainability, yet this dimension remains underexplored—especially in large, diverse economies like China. This study addresses that gap by analyzing household carbon emissions across 29 Chinese provinces from 2000 to 2022, focusing on regional differences and convergence patterns. Using spatial and convergence models, we find persistent clustering—where provinces with high or low emissions group together—though these patterns shift gradually. Emissions have generally risen nationwide, with convergence trends emerging in the east, central, south, and north, while the west and northeast show inconsistent dynamics. Notably, emissions in one province are influenced by those in neighboring provinces, particularly in central China, due to close economic and energy ties. Industrial structure slows convergence at the national level, whereas stronger economic development, better education, and higher industrialization contribute to narrowing regional disparities—especially in southern China. These findings offer new insights for designing region-specific strategies that align household emissions management with China’s broader climate and sustainability goals. Full article

This paper investigates the distributional drivers of carbon emissions in Türkiye, focusing on how household income and consumption patterns influence carbon footprints. Utilizing the microsimulation model, we integrate detailed expenditure data from Türkiye’s 2019 Household Budget Survey with the 2016 Input-Output table from the World Input-Output Database. This approach enables the simulation of both direct and indirect CO₂ emissions, providing a comprehensive analysis of the environmental impact of household consumption across different income groups. Our findings reveal significant disparities in carbon emissions, highlighting the complex interplay between income levels, consumption behaviors, and environmental outcomes. This research underscores the importance of considering distributional effects in the design of carbon pricing policies to ensure equity and effectiveness in emission reduction strategies. This study focuses on understanding household distributional drivers of carbon emissions. Full article

Nigeria is among the countries highly vulnerable to climate change impact. Thus, there has been growing emphasis on the pursuit of decarbonization and net-zero (net-zero transition) strategies. The aim of this work is to review major concepts in research publications associated with climate change mitigation in Nigeria. The literature search was conducted on the Scopus database using relevant keyword operators. Mixed methods were adopted to conduct bibliometric, text mining and content analysis. Bibliometric software (VOSviewer) was used. The research objectives were to identify how net-zero transition research has evolved in Nigeria; their important research themes and trends in Nigeria, and potential directions for future research on achieving them in Nigeria. The results show that the number of publications in the field has been increasing, with 87% of the articles included in the dataset published between 2016 and 2024. Through data clustering, eight clusters of articles were identified, namely (i) the renewable energy, economic growth and emission reduction nexus (ii) energy transition in the Nigerian power system, (iii) policy drivers (socio-technical and economic) for a cleaner energy system, (iv) energy transition governance, (v) hybrid renewable energy systems, (vi) low-carbon transition, (vii) energy efficiency and low-carbon growth and others. By checking through the keywords used by authors, it appears that the most popular keywords are carbon neutrality, hydrogen, biomass, circular economy, and electric vehicles. These keywords further highlight areas of research interests. Some of the potential future directions identified include the need for effective research communication and strong cooperation between academia and relevant CC policy-making bodies to translate scientific research into evidence-based policies and actionable frameworks; tiered subsidies or tax rebates to low-income households to promote CC mitigating technologies and align CC objectives with social equity; and others. Although this work focuses solely on Nigeria, the country shares similar characteristics with many sub-Saharan African countries, and some others in the global South. Accordingly, the findings will be relevant to those areas, with some unique adaptations. Full article

About 50% of Italian households’ overall energy consumption is satisfied by natural gas, mainly for space heating, leading to substantial CO₂ emissions. In Italy’s mild climate, fourth-generation district heating (4GDH) networks coupled with renewable energy sources (RESs) could represent a viable option for reaching the ambitious space heating decarbonization objectives set by the EU. In this paper, such a decarbonization pathway, consisting in a centralized heat pump (HP)-powered 4GDH network, with and without the addition of a distributed PV plant, is assessed and compared with the individual natural gas boilers-based Italian reference scenario. A cluster of buildings, comprising 200 dwellings, representative of common households in Rome, has been chosen as the case study. Starting from the cluster’s hourly space heating demand, a semi-dynamic MATLAB/Simulink model has been developed to size the technological components and evaluate their performance with respect to outdoor environmental conditions. The scenario comparison is carried out by means of techno-economic and environmental indicators: the levelized cost of heat (LCOHE), CO₂ emissions, and carbon avoidance cost (CAC). Moreover, a sensitivity analysis has been carried out to address the uncertainty regarding the main economic parameters, namely the electricity and natural gas price and the HP and DH investment cost. The results show that 4GDH-based layouts significantly reduce CO₂ emissions, at the expense of the LCOHE. The sensitivity analysis highlights how a significant reduction in both the electricity price and the DH network capital cost are required for achieving price parity with the fossil-fuel based scenario. Full article