Search Results (831)

Traditional household-based decentralized agro-processing is an important source of air pollution in rural China, yet the pollution–carbon co-benefits of centralization remain poorly quantified. In this paper, a LEAP-based scenario framework (2018–2035) is developed for Yibin’s Camphora longepaniculata essential oil processing industry under four scenarios: baseline, policy (27% centralization share), enhanced scenario 1 (ENH1; 50% centralization share), and enhanced scenario 2 (ENH2; 50% centralization share with a 2% efficiency retrofit). The framework simulates energy demand and emissions of CO₂, PM₁₀, SO₂, NOx, and VOCs based on specified emission factors and technological assumptions. The results indicate that centralization reduces unit energy consumption by 51.20% and CO₂ intensity by 63.12%. The 27% penetration rate (current policy) lowers emission levels but cannot alter the growth trajectory during output expansion, whereas the 50% penetration rate achieves net CO₂ and energy reductions of 31.56% and 25.60%, respectively, relative to the baseline. All pollutants exhibit synergy elasticity coefficients (ε) above unity (PM₁₀ > SO₂ > VOCs > NOx); these coefficients are governed by the emission factor structure rather than the penetration rate. Thus, scaling centralization drives absolute reductions, whereas co-benefit enhancement requires process or end-of-pipe upgrades. All of the reported results are scenario-based model outputs, not field measurements, and biogenic CO₂ is included only for cross-scenario comparability, not as a climate impact claim. Full article

The construction phase of sports buildings is characterized by high carbon emission intensity, yet existing studies have largely focused on operational energy consumption and static life-cycle accounting, with no systematic investigation of the dynamic interactions among labor, materials, machinery, and environmental factors during construction. To fill this gap, this study develops an agent-based modeling (ABM) framework for a university natatorium in Shaanxi, China, to assess carbon emissions during the civil engineering construction phase and analyzes the independent and synergistic effects of labor, materials, machinery, and environmental factors. The simulation results show that, among single factors, material recycling achieves the highest reduction efficiency (57.93%, under the avoided-burden approach, representing a technical upper-bound estimate), followed by labor skill improvement (3.08%) and machinery maintenance (0.26%), while adverse weather increases carbon emissions by 13.20%. Multi-factor synergy analysis reveals that labor skill improvement buffers weather-induced increases (synergy: +610.49 t, 4.26%), and the full-intervention scenario achieves a 52.33% net reduction under adverse weather, though weather impacts cannot be fully offset. The proposed framework provides methodological support and a decision-making basis for low-carbon construction planning of natatoriums and similar buildings. Full article

Computing capacity has become a metered factor of production for digitally intensive enterprises, yet its consumption exhibits strong temporal heterogeneity—tidal intraday cycles, weekly contrasts, seasonal surges, and project-driven regime shifts—so that individually provisioned capacity is structurally underutilized. This paper proposes a park-level computing power sharing center (CPSC) as an institutional mechanism that converts the temporal complementarity of co-located enterprises into measurable cost savings. We develop a general mode-design framework that separates CPU core-hours from GPU card-hours, characterizes demand via deterministic tides and stochastic modulations, and derives optimal pooled capacity commitments through a newsvendor-type quantile condition. A parametric calibration protocol maps observable temporal features—peak-to-trough ratios, inter-tenant phase spreads, and residual volatility—into closed-form diversity-factor expressions with Monte Carlo confidence intervals. The procurement model covers a multi-option contract menu (on-demand, one–three-year reserved instances, savings plans, and spot), region-specific pricing, hardware class tariffs, and ancillary costs, including network egress, migration, and data sovereignty compliance; benefits are measured relative to each tenant’s individually optimal reserved portfolio, not naive retail procurement. A mechanism design analysis incorporating Shapley value allocation, Bayesian incentive compatibility, and penalty structures ensures individual rationality and robustness to misreporting and strategic load shifting. We further develop an energy model—with utilization-dependent power draw, facility PUE, embodied carbon, and marginal grid emission factors—showing that financial savings translate into genuine emission reductions only when pooling enables physical capacity retirement rather than mere billing reallocation. The framework is applied to twenty representative Chinese parks spanning seven functional categories; all park-level data are reconstructed from public sources using the calibration methodology, and the reported figures are model-derived projections, not empirical measurements. The model yields procurement saving estimates of 4.6–20.2% relative to individually optimal reserved-procurement portfolios, with high-diversity parks at the upper end. Sensitivity analyses across regional tariffs, hardware mixes, and cross-country utilization benchmarks (Uptime Institute, US DOE, EU Commission) confirm robustness and delineate boundary conditions. This paper concludes with a data provenance taxonomy and a phased implementation roadmap. Full article

Natural gas consumption in urban residential buildings is important for household energy affordability and the low-carbon transition of the residential sector. Using panel data from 30 Chinese provinces from 2005 to 2020, this study employs a panel regression model to examine the impact of residential natural gas prices (NPs) on per capita natural gas consumption (NGC) in urban residential buildings. We apply the model to estimate price elasticity and analyze the transmission mechanism of energy consumption intensity (EI), while examining the differentiated characteristics of price effects from both regional and urbanization-stage perspectives. The main findings are as follows: (1) NP shows a significantly negative association with residential NGC, with a price elasticity of −1.182; (2) EI serves as a potential transmission channel between NP and NGC, with its indirect effect offsetting 12.5%; (3) heterogeneity analysis shows that price elasticity is statistically significant only in the western region. Across urbanization quartiles, the absolute magnitude of price elasticity is greatest in Q2. This study provides empirical evidence to optimize tiered natural gas pricing, refine targeted subsidy policies, and inform energy utilization policies in urban residential areas. Full article

Seawater reverse osmosis (SWRO) desalination is electricity-intensive, making its environmental performance highly dependent on the power supply. This study couples site-specific energy-system modelling with life cycle assessment to examine how renewable integration changes both total impacts and life-cycle hotspots. A modelled SWRO plant with a specific electricity consumption of 3.4 kWh m⁻³, derived from a process model for Mediterranean feedwater at 48% recovery with energy recovery devices, was assessed in Gela and Trapani, two grid-connected coastal sites with contrasting wind resources, and Lipari, a non-interconnected island with carbon-intensive backup generation. Grid-only, photovoltaic (PV)-grid, wind-grid, and PV-wind-grid configurations were modelled in HOMER Pro without storage and with excess electricity limited to 13%, then evaluated in SimaPro using Environmental Footprint 3.1. Hybrid configurations supplied 46.7%, 64.4%, and 53.3% renewable electricity in Gela, Trapani, and Lipari, reducing climate-change impacts by 30%, 42%, and 45%, respectively. Renewable integration also lowered fossil resource use, whereas PV-containing scenarios increased land and mineral/metal resource use. As electricity-related impacts declined, chemical consumption became the main non-energy hotspot, particularly for ecotoxicity, freshwater and eutrophication. Environmental performance therefore depends not only on renewable penetration, but also on technology choice and the residual electricity supply. Comprehensive system boundaries are essential when planning lower-carbon desalination for coastal and island water security. Full article

Understanding the relationship between economic growth, energy use, and environmental degradation remains a key challenge for resource-dependent economies undergoing energy transition. This study re-examines the effects of economic growth, energy consumption, renewable energy, and capital formation on CO₂ emissions in Kazakhstan using annual data for 1992–2024. To account for structural changes and ensure robust inference, the analysis employs the Bai–Perron structural break test, the Autoregressive Distributed Lag (ARDL) approach, and alternative cointegration estimators including Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegrating Regression (CCR). The results reveal a stable long-run relationship among the variables. Economic growth and energy consumption significantly increase CO₂ emissions, indicating the persistence of a carbon-intensive growth pattern in Kazakhstan. The effect of renewable energy is not robust across estimation techniques and does not provide consistent evidence of an emissions-reducing impact. Capital formation also shows limited explanatory power for long-run environmental outcomes. Overall, the findings suggest that Kazakhstan has not yet achieved a decoupling between economic growth and environmental degradation. The study contributes to the literature by jointly incorporating structural breaks, multiple cointegration estimators, and an extended sample period within a country-specific framework. The results imply that expanding renewable energy capacity alone may be insufficient to improve environmental quality, highlighting the need for broader structural transformation and low-carbon development strategies. Full article

Background: Following industrial energy consumption, freight transportation represents the second-largest source of greenhouse gas emissions nowadays. However, although several metrics for the supply chain’s profitability already exist, indicators for its environmental performance are currently lacking. Methods: Hence, a preliminary review of metrics for green logistics was carried out in order to select a comprehensive indicator for more detailed studies. The effectiveness of the proposed metrics was tested on a real industrial case concerning the Messebo cement factory in Ethiopia to explore potential advances in developing countries, where the availability of renewable energy sources is extremely high but infrastructures for their exploitation are absent. Then, the constrained route optimization problem was solved to investigate the implementation of potential improvements. Results: The outcome of this study shows that delivery route optimization improves transportation environmental performance, on average, by 15%, while the replacement of conventional freight trucks with electric vehicles can abate up to 90% of the overall carbon footprint. In both cases, all constraints were satisfied within the required time window. Conclusions: In conclusion, this work proves the effectiveness of green logistics indicators and represents a first step towards supply chain optimization for carbon-intensive sectors in developing countries. Full article

With accelerating global warming, reducing carbon emissions has emerged as a core task of global climate governance. In this process, digital infrastructure acts as a crucial catalyst for achieving sustainability goals. Drawing on a panel dataset covering 287 Chinese prefecture-level cities between 2010 and 2023, this paper examines how Broadband China policy influences carbon dioxide (CO₂) emission intensity via a difference-in-differences approach. The outcomes show that Broadband China policy significantly reduces CO₂ emission intensity, with pilot cities exhibiting a 5.01% decrease relative to non-pilot cities. This effect operates primarily through promoting green technological innovation and reducing energy consumption intensity. The reduction is more pronounced in large-scale and high developed regions, whereas the effect remains limited in small-scale and less developed regions. This study contributes to the literature by using carbon emission intensity, a structural indicator that better reflects the efficiency of low-carbon transition, to examine the digital economy and environmental governance. It further provides empirical support for other developing countries seeking to advance emission reduction through digitalization. Full article

The building industry is the largest consumer of energy and the largest source of carbon emissions, and the sustainable development of building envelopes is thus inevitable. Among the facade systems, metal cladding materials offer a number of benefits such as high durability, architectural freedom, and recyclability; however, the choice of these materials needs to be based on several performance aspects. This research presents a novel comprehensive approach for analyzing metal cladding options in a health care building through the integration of building energy simulation and multi-attribute decision analysis (MADA) techniques. A primary health care facility in Istanbul, Türkiye, was used as a case example. Eight metal cladding materials—steel, aluminum, copper, zinc, titanium, stainless steel, Corten steel, and magnesium alloy—were evaluated against various wall and insulation combinations. The assessment combined energy with physical–mechanical, thermal, acoustic, and sustainability indicators such as density, thermal conductivity, Young’s modulus, damping capacity, traffic noise insulation, service life, and recyclability. A series of building energy simulations was performed to estimate the effect of facade design options on yearly energy consumption, and the resulting data set was analyzed based on the CRITIC, LOPCOW and ALPAS methods. This method allows the comprehensive evaluation of metal cladding material on energy efficiency, structural strength, acoustic performance, durability, and circularity simultaneously. The results provide a practical decision support framework for sustainable facade material selection in health care and other energy-intensive buildings. Titanium emerged as the optimal metal cladding material, distinguished by its superior combination of low thermal conductivity, damping capacity, and recyclability under both CRITIC and LOPCOW weighting schemes. Comparative analysis across nine MADA methods (ρ = 0.932) and sensitivity analysis over 70 scenarios confirmed the robustness of this finding, with titanium retaining first place in 64 out of 70 perturbation scenarios. These outcomes provide materials engineering insight into how the mechanical, thermal, and durability characteristics of structural metals and alloys translate into differentiated in-service performance, offering evidence-based guidance for metal selection in facade applications. The outcomes reported relate to one health care facility located in Istanbul and demonstrate the potential of the novel framework for the specific investigated case but are not intended to be generalized across all building types and climatic zones or to provide universally applicable material rankings. Full article

Agricultural production is an important source of global carbon emissions, yet differences in system boundaries and emission factors among previous studies have limited comparisons across crops and regions. This study investigated the distribution, emission sources, and regional disparities of agricultural carbon emissions across 31 major crop-producing provinces in China, using a unified life cycle assessment (LCA) framework based on agricultural input, crop production, and agronomic data in 2024. Carbon emissions per unit area (CEA) and per unit yield (CEY) were quantified under consistent accounting boundaries, and the contributions of different emission sources together with their spatial characteristics were discussed. CEA generally showed higher values in the central and eastern regions of China and Xinjiang, but lower values in southwestern and northeastern China. Xinjiang contributed the highest total carbon emissions (about 1.6 × 10⁵ t), primarily because extensive cotton cultivation requires intensive irrigation, mechanized operations, and plastic-film mulching, leading to high emissions from fertilizer use, energy consumption, and agricultural film. Rice exhibited the highest carbon emissions (accounting for 30% of all 11 types of crops), followed by cotton and tobacco, while soybeans, rapeseed, and sugar beets had relatively low emission intensities. Fertilizer production and application were the dominant emission sources for most upland crops, while methane emissions from flooded paddy fields accounted for the largest share of rice carbon emissions. Spatial clustering analysis further indicated that high-emission regions were concentrated in central and eastern China, while northeastern China formed distinct low-emission clusters. This study provided a consistent assessment of carbon emissions from major crops across China, offering a reference basis for formulating emission reduction strategies for different crops and regions. Full article

Understanding building operational carbon dioxide (CO₂) emissions is essential for sustainable urban planning, yet variations in emissions across building types remain poorly characterized. This study integrated top-down and bottom-up approaches to estimate building-scale CO₂ emissions, capturing fine-scale emission patterns while maintaining consistency with aggregate energy statistics. Taking Guiyang as a case study, electricity consumption was simulated using the Designer’s Simulation Tool (DeST), while natural gas (NG) and liquefied petroleum gas (LPG) consumption were disaggregated using an area-proportional allocation method. Emission factors were then applied to estimate monthly CO₂ emissions. Results showed that monthly building CO₂ emissions ranged from 0.81 to 1.09 million tons. Significant spatial disparities were observed, with core districts contributing more than 22% of total emissions, whereas peripheral districts accounted for only approximately 6%. Residential buildings produced the highest total emissions, averaging 433 thousand tons per month, while shopping malls showed the highest emission intensity, reaching 8.08 kg/m² in July. The different building types and different seasons had different emissions, with residential buildings showing higher emissions in winter, whereas hotels and shopping malls experienced higher emissions during summer. The proposed framework extends existing approaches, providing reliable building CO₂ data for urban low-carbon planning and targeted mitigation. Full article

Against the background of global production-network restructuring, low-carbon transition, and rising external uncertainty, this study examines the effect of green infrastructure investment on urban industrial chain resilience. Using panel data for 285 Chinese prefecture-level cities from 2012 to 2024, industrial chain resilience is measured from the dimensions of industrial diversification and urban innovation capacity. Double machine learning is employed for baseline estimation, supplemented by mediation analysis, threshold regression, spatial econometric analysis, and a series of robustness tests. The results show that green infrastructure investment significantly enhances industrial chain resilience, and the finding remains robust to alternative model specifications, cross-fitting settings, generalized propensity score weighting, continuous-treatment entropy balancing, winsorization, and the exclusion of pandemic-period observations. Resource allocation efficiency plays a partial mediating role in this relationship. The threshold analysis identifies a significant nonlinear effect associated with energy consumption intensity, with the positive effect of green infrastructure investment being stronger below the estimated threshold and weakening above it. Spatial analysis further shows significant spatial dependence in both green infrastructure investment and industrial chain resilience, together with positive spillover effects on neighboring cities. These findings highlight the importance of improving green infrastructure investment efficiency, strengthening factor allocation, and promoting regional coordination in enhancing urban industrial chain resilience. Full article

Hospital buildings (HBs) are among the most energy-intensive public buildings, yet their life-cycle carbon characteristics, emission drivers, and long-term mitigation potential remain insufficiently quantified. This study establishes a comprehensive life-cycle carbon assessment framework for HBs based on life cycle assessment (LCA), using a Grade-A tertiary hospital in Shenzhen, China, as a case study. The framework quantifies carbon emissions across the materialization, operation, and demolition stages, and estimates operational emissions from public hospital buildings at the city scale. Logarithmic Mean Divisia Index (LMDI) decomposition and Long-range Energy Alternatives Planning (LEAP) modeling were subsequently applied to identify historical drivers and evaluate future mitigation pathways. The results show that the case hospital generated approximately 0.57 Mt CO₂e of gross life-cycle carbon emissions over a 50-year service life, with the operational stage dominating approximately 88% of net emissions. Electricity consumption accounted for 94% of operational energy-related emissions, while HVAC systems and the Diagnostic departments were identified as major carbon hotspots. At the city scale, the gross operational emissions of 73 public hospitals in Shenzhen were estimated at approximately 0.74 Mt CO₂e in 2020 within the defined accounting boundary. For the broader citywide hospital sector, LMDI analysis revealed that annual operational emissions increased from approximately 0.25 Mt CO₂e in 2006 to 0.91 Mt CO₂e in 2020, primarily driven by healthcare service demand and hospital infrastructure expansion, whereas the declining operational carbon emission coefficient provided a partial offset. LEAP scenario analysis further demonstrated that net operational emissions peaked in 2050 under BS and in 2030 under SI and SII. Under SIII, emissions declined continuously from the 2020 base-year level to approximately 0.29 Mt CO₂e in 2060, representing a reduction of approximately 68%. These findings highlight the necessity of coordinate building energy optimization, healthcare infrastructure development, and energy system decarbonization for low-carbon transformation of hospital buildings. Full article

Over the past two decades, the world has experienced a significant and relentless increase in environmental degradation, measured through carbon emissions (CO₂). These emissions have been one of the persistent global concerns. South Africa boosts abundance of natural resources and some of the world’s most substantial mineral deposits, endowments in the form of precious metals, diamonds and gold. The paper aims to examine the impact of socio-economic and energy-related factors on environmental degradation from a South African perspective. Using multivariate annual data spanning from 1991 to 2022, the Autoregressive Distributed Lag Model (ARDL) was employed to determine both short-run and long-run impact of financial development (FD), renewable energy (RE), non-renewable energy (NRE), unemployment rate (UNE), economic growth (GDPPC), and population growth (PoPG) on CO₂ emission. The results show that NRE remains a dominant driver for environmental degradation, while RE is positively associated with emissions under current system conditions. FD exhibits short-run emission-intensity but long-run mitigation effects. The results suggest a need for relevant policymakers to prioritize coal displacement, stimulate economic growth and promote access to green financing, and related technologies and consumption, to enhance and promote environmental quality in South Africa. The conclusion is that South Africa’s energy-economy nexus is still at a transitional stage, where targeted policy intervention and structural reform are essential to accelerate the shift towards a low-emission economy. Future research can extend the analytical depth by exploring asymmetries, disaggregating fossil fuels, and incorporating broader environmental indicators. Full article

The production of hot mix asphalt (HMA) is energy-intensive, resulting in carbon dioxide (CO₂) and greenhouse gas (GHG) emissions. The primary energy source (accounting for over 97%) and emissions source is the rotary drum employed for the drying and heating of the aggregates. Quantifying the CO₂ emissions associated with combustion is of crucial importance in order to facilitate a more profound comprehension of the environmental impacts of HMA production. The objectives of this study are to develop a methodological framework for the quantification of combustion-related carbon dioxide emissions in the context of asphalt production. The proposed framework investigates three complementary approaches: (i) an energy-balance-based thermal energy (TE) model, (ii) recordings of fuel consumption and (iii) direct measurement of exhaust-gas composition. By applying these methods in parallel and cross-comparing their results batch by batch, the framework enables reliable verification of actual CO₂ emissions from the module A3—production stage of asphalt manufacturing. In this stage, the predominant source of greenhouse gases is fuel combustion during aggregate drying and heating. A comprehensive set of data was collected from two HMA batch plants, each operating under distinct conditions. The parameters considered included fuel type, asphalt mixture type, asphalt production time, aggregate moisture content, mixing temperature, and production rate. The TE model demonstrated a robust linear correlation with measured energy consumption (R² = 0.97), and fuel-based CO₂ estimates exhibited minimal discrepancy compared to direct exhaust-gas measurements on average (mean difference 1.0%; t-test p = 0.674). However, systematic discrepancies were observed between the two plants (with overestimation of up to 20% at one plant (AP1) and underestimation of up to 12% at the other (AP2)). This demonstrates that energy-based CO₂ estimation methods require plant-specific calibration against direct measurement before they can be reliably applied in life cycle assessment (LCA) and environmental product declaration (EPD) practice. Measured CO₂ emission intensities ranged from 17.39 to 21.76 kg/t at AP1 and from 16.05 to 18.44 kg/t at AP2; the casing-losses factor of the TE model was calibrated to CL = 23% for the studied diesel-fired plants (mean deviation +0.4% from measured energy); and aggregate moisture content explained 74% of the variance in measured energy consumption (R² = 0.743). Full article