Search Results (158)

The building sector faces an urgent challenge in balancing carbon neutrality goals with natural resource conservation. This study constructs a three-dimensional dynamic coupling model integrating Life Cycle Assessment, carbon footprint, and emergy analysis to evaluate the sustainability of a hydrogen–solar–geothermal hybrid energy system for an ecological office building in China’s hot summer and cold winter climate zone over a twenty-year horizon. The model incorporates dynamic factors including grid decarbonization, equipment efficiency degradation, and replacement cycles to overcome the systematic bias inherent in static LCA. Results reveal a significant trade-off: the hybrid system achieves a 29.8% reduction in global warming potential with a seven-year carbon payback period, yet non-renewable resource consumption doubles and resource scarcity damage increases by 173%. The carbon payback trajectory exhibits non-monotonic fluctuation, with electrolyzer replacement in year ten generating 360 tonnes of additional emissions that nearly reset the cumulative net value to zero. Multi-objective optimization identifies photovoltaic capacity as the system baseline (170–210 kW) and electrolyzer capacity as the primary regulating variable (35–62 kW), with the TOPSIS-recommended compromise solution of 200 kW photovoltaic, 50 kW electrolyzer, 30 kW fuel cell, and 32 m³ hydrogen storage achieving annual carbon emissions of 280 tonnes and a 33.3% reduction. Carbon pricing exhibits a nonlinear leverage effect with an incentive threshold of 200 RMB per tonne, substantially above China’s current 60–80 RMB per tonne level. This study concludes that while hydrogen–solar–geothermal hybrid systems offer substantial climate benefits, their comprehensive sustainability depends on proactive management of material scarcity costs, precise planning of equipment replacement cycles, and coordinated multi-level policy instruments. The findings provide methodological foundations for transitioning building carbon neutrality assessment from static LCA to dynamic coupling frameworks and from single carbon metrics to integrated carbon-resource-cost evaluations. All quantitative results presented herein are derived from this specific case study under the stated assumptions and parameter values; generalization to other building types or climate zones requires recalibration. Full article

Coordinating air pollution control with carbon mitigation is a central challenge for urban sustainability. However, there is currently a lack of sufficient research on whether digital infrastructure can complement environmental regulations to enhance the synergy of pollution and carbon emission reduction (SPCER). Using the overlapping implementation of China’s Key Air Pollution Control Zones (KAPCZs) and Broadband China (BC) policies as an empirical policy setting, this study applies a partially linear Double Machine Learning framework to panel data for 282 Chinese cities from 2006 to 2023. The results show that the dual-policy pilots significantly improve urban SPCER, while a unified interaction test further provides statistical evidence of a positive synergistic effect between the two policies. Mediation analyses provide evidence consistent with Green Technological Innovation, industrial structure upgrading, and green finance development as potential transmission mechanisms. Formal cross-group tests reveal significant heterogeneity across regions and city characteristics, with larger effects in highly urbanized, low industrial development, non-old industrial base, central, and non-resource-based cities. Spatial Durbin Model estimates further indicate positive spillovers to neighboring cities. These findings demonstrate the potential value of coordinating environmental regulation with digital infrastructure and provide evidence for differentiated policy design and cross-regional collaboration in urban digital–green transitions. Full article

This research analyses the heterogeneous effects of digital infrastructure on sustainable economic growth in five developing countries (Egypt, India, Kenya, Saudi Arabia and Sudan) using the period 2014–2025. We use panel fixed effects with Driscoll–Kraay standard errors to investigate the effect of internet penetration, mobile broadband and fixed broadband on the GDP per capita growth. Our results suggest that digital infrastructure has a statistically and economically significant impact on economic growth. Internet penetration yields the largest benefits, followed by mobile broadband, whereas fixed broadband is not statistically significant in the full model, which is reflective of limited access in these countries. An exploratory Random Forest analysis with the important caveat of limited sample size suggests that internet penetration is the most important predictor of growth, followed by mobile broadband and human capital. However, these machine learning results should be considered exploratory given the small sample (N = 60, or N = 48 when excluding Sudan) and should not be over-interpreted. Our heterogeneity analysis finds that internet penetration drives growth in middle-income countries (Egypt, India), while mobile broadband drives growth in low-income countries (Sudan, Kenya). The moderation by human capital is large: the marginal impact of internet penetration more than doubles once average education exceeds six years. However, we note that this threshold is illustrative, based on the distribution of the sample, and not necessarily a policy cutoff. The results have implications for SDG 4 (quality education), SDG 9 (infrastructure and innovation) and SDG 10 (inequality reduction). However, with our purposively selected five-country sample, these results are better considered as case-based evidence rather than statistically representative of all developing economies. The stark digital divide is evident in the 87% internet penetration in MENA countries compared to 44% in Sub-Saharan Africa and underscores the need for context-specific policy approaches. We suggest that low-income countries focus on expanding mobile broadband and middle-income countries make complementary investments in internet infrastructure and human capital, but emphasize that these policy suggestions are indicative rather than conclusive. Full article

Background and aims: Adolescent mental health is a core component of Goal 3 “Good Health and Well-being” in the SDGs. This study explores the relationship between academic burden and adolescent mental health under China’s “Double Reduction” policy, as well as the roles of perceived stress and self-efficacy in this relationship. Method: Using the purposive sampling method, 842 samples of junior high school students in Nantong City, Jiangsu Province, China, were effectively collected. Statistical analyses included a common method bias test, correlation analysis, multiple linear regression and the Bootstrap method. Results: Studies have shown that under the “Double Reduction” policy, there are pairwise correlations between academic burden, perceived stress, self-efficacy and adolescent mental health. After controlling for sociodemographic characteristic covariates, academic burden remained significantly negatively correlated with adolescent mental health (β = −0.277, p < 0.001), and the negative correlation degree between in-school academic burden and mental health (β = −0.295, p < 0.001) was greater than that of out-of-school academic burden (β = −0.070, p < 0.05). Furthermore, perceived stress and self-efficacy exhibited a composite correlational linkage pattern within this relationship—simultaneously encompassing both parallel and serial multiple associative pathways. Conclusion: Under the “Double Reduction” policy, academic burden and Chinese adolescents’ mental health show a significant negative association, with perceived stress and self-efficacy acting as key bridges. Full article

Non-communicable diseases (NCDs) represent a growing challenge for developing countries, as they are among the leading causes of morbidity and mortality within the working-age population. Beyond their effects on health outcomes, NCDs are associated with reductions in labor productivity, which may influence long-term development dynamics through human capital. This study estimates the conditional labor productivity penalty associated with Non-Communicable Diseases (NCDs) among employed workers in Mexico using an observational high-dimensional framework. By addressing high-dimensional confounding under the Conditional Independence Assumption (CIA), the analysis isolates the relationship between NCD status and hourly labor earnings. Using microdata from the Mexican National Survey of Household Income and Expenditure, labor productivity is defined as the ratio of labor income to hours worked. To obtain robust causal estimates, a Double Machine Learning (DML) framework is implemented, integrating regularized regression and machine learning algorithms to control for high-dimensional confounding factors. These covariates include socioeconomic, demographic, and health-related variables, allowing for flexible adjustment of observable heterogeneity across individuals. The empirical findings demonstrate a statistically significant conditional negative penalty of −11.44% (p = 1.03 × 10⁻¹¹, 95% CI: [−14.59, −8.40]) on hourly labor earnings under the cross-fitted DML specification using XGBoost and Random Forest. To resolve an observational measurement artifact where chronic individuals report artificially inflated hourly wages due to severe hours contraction, a presenteeism adjustment factor ($\theta *=0.8125$) was calibrated. Incorporating this presenteeism correction, the aggregate national economic burden among the employed population is estimated at approximately 496.82 billion pesos annually. The findings suggest that NCDs constitute a relevant constraint on labor productivity at the microeconomic level. The study highlights the importance of prevention-oriented health strategies, workplace health promotion, and early disease management. From a methodological perspective, the results illustrate the usefulness of Double Machine Learning for purging high-dimensional confounding in cross-sectional survey data, providing robust conditional empirical benchmarks for public health policy. Full article

Container sea–rail intermodal transport is pivotal to the low-carbon transformation of global supply chains. However, traditional performance evaluation systems are prone to circular reasoning fallacies due to the nesting of input and output indicators and frequently suffer from spurious regression when analyzing high-dimensional macro time series under small-sample constraints. To address these endogeneity and attribution challenges, this study proposes a four-step progressive causal inference framework. Taking Tianjin Port—a pioneering hub of China’s “road-to-rail” freight restructuring policy—as the empirical subject, we use quarterly operational data covering a complete cycle from 2017Q1 to 2024Q4. First, we construct a strictly exogenous high-quality development index based on turnover efficiency, logistics cost reduction, and carbon emission mitigation, which completely isolates scale input factors. Second, from an initial pool of 35 operational and macroeconomic indicators, 17 candidate variables are rigorously pre-screened according to statistical consistency and logistics system theory. Third, an adaptive Double Machine Learning (DML) model integrated with leave-one-out cross-fitting is applied to disentangle complex collinearity among variables. The results show that DML effectively eliminates confounding noise, accurately identifies 15 true causal drivers, and excludes spurious correlations such as redundant macro-infrastructure investment. Furthermore, a causally weighted composite index reveals that the intermodal system exhibits strong resilience to global supply chain fluctuations and has undergone a four-stage evolution. Its development momentum has fundamentally shifted from extensive scale expansion to a refined mode driven by the synergy of efficiency and service quality. This study provides a robust methodological paradigm for port performance evaluation and targeted decision support for resource allocation. Full article

Catalyst activity loss reduces both the performance and operating flexibility of industrial sulfur dioxide converters, yet its consequences are rarely assessed beyond conversion declines. This work develops an activity-loss vulnerability framework for a four-bed double-contact SO₂ converter model evaluated against an industrial fresh-catalyst benchmark and applies it to four prescribed activity scenarios ($a=1.0$, 0.8, 0.6, 0.4). At the reference inlet-temperature policy, reducing activity from $a=1.0$ to $a=0.4$ lowered conversion from 99.758% to 96.812%, increased outlet SO₂ slip from 230 to 2960 ppmv, and raised the hotspot from 613.7 to 660.3 °C, exceeding the adopted illustrative limit of 650 °C. Sensitivity, vulnerability, hotspot risk, and feasible-region maps show that the prescribed activity loss progressively shrinks the permissible operating envelope and creates a coupled productivity–emissions–thermal-safety tradeoff. A non-uniform activity profile at the same mean activity as uniform $a=0.6$ produced a hotspot that was 9.3 °C higher, demonstrating that average activity alone is insufficient for thermal-risk assessment. Finally, a scenario-relative Operating Efficiency Reduction Index (OERI) integrates conversion loss, SO₂-slip increase, and thermal-margin loss into an illustrative scenario-screening score. The results show that catalyst activity loss should be assessed as a coupled performance, emissions, and operational-vulnerability problem rather than conversion decline alone. Full article

Environmental protection has become a key priority for the Chinese government. This study examined the symmetric complementarity of spatial layouts between inter-city high-speed rail and urban subway systems. Their co-location formed a structurally symmetric low-carbon travel chain that bridged long-distance and short-distance trips. The study evaluated their joint effect on urban carbon productivity within a difference-in-differences framework. The baseline two-way fixed-effects DID estimate showed that the simultaneous operation of HSR and subway systems significantly improved urban carbon productivity, with an average increase of approximately 10.51% in treated cities relative to non-treated ones. This core finding remained qualitatively robust across a series of tests, including conditional coarsened exact matching (CEM), placebo simulations, sample exclusions, and decomposition of joint effects relative to individual rail impacts. Complementary nonparametric causal forest estimates yielded smaller but still significant effect sizes, providing supportive evidence for causal validity under weaker functional form assumptions. Heterogeneity analysis based on dynamic GDP grouping revealed that high-GDP cities experienced stronger emission reductions, while low-GDP cities benefited more from the economic growth dimension of carbon productivity. Empirical patterns aligned with the theoretical prediction of the transportation substitution mechanism. The positive effect of dual rail availability on carbon productivity was stronger in cities with higher private car stock, as shown by double machine learning interaction models. This finding only provided indirect evidence and did not constitute direct proof of actual modal shift behavior. The results exhibited magnitude asymmetry between parametric and nonparametric methods, yet all estimates consistently pointed to a positive qualitative direction. This consistency revealed directional symmetry in the causal conclusion. The study contributed by (1) examining the combined carbon productivity effect of co-located HSR and subway systems, (2) identifying the moderating role of private car stock in the transportation substitution mechanism, (3) revealing development-stage-based heterogeneity, (4) and combining machine learning methods with parametric causal inference as complementary robustness evidence. Policy implications suggested that high-GDP cities should prioritize expanding dual rail coverage and optimizing connections to amplify emission reductions, while low-GDP cities should focus on improving HSR connectivity with existing public transit systems and fostering low-carbon industrial development in line with local conditions. Full article

As the key driver of economic development, sci-tech finance presents a new opportunity for China’s economic green transformation. This paper employs the difference-in-difference method to examine the influence of sci-tech finance policies on urban carbon emission efficiency. The results show the following: (1) Generally, the technology finance policy can notably enhance urban carbon emission efficiency, and this conclusion remains valid after a series of robustness checks. (2) Mechanism tests indicate that sci-tech finance can promote low-carbon development via innovation, financing, and energy structure channels. (3) Heterogeneity tests reveal that sci-tech finance has a more pronounced impact on emissions reduction in the eastern regions, areas with strict environmental regulations, regions with a higher degree of marketization, and non-resource-based cities. (4) A spatial Durbin model was employed to analyze the spatial spillover effects of science and technology finance. (5) The Double Machine Learning (DML)model was used for verification and inspection, and found that sci-tech is conducive to improving the carbon emission efficiency of cities. Finally, this study offers policy recommendations, including formulating region-specific implementation guidelines for the growth of sci-tech finance, taking into account the distinct features of each area. Full article

Background: Buildings and construction account for a substantial share of global energy use, carbon dioxide emissions, and material extraction, making both operational and embodied carbon central to climate-resilient building policy. Methods: This article is framed as a critical scoping review with a structured narrative synthesis. It synthesizes peer-reviewed studies, standards, and policy reports published mainly from 2020 to June 2026, while retaining older foundational standards where they define life-cycle boundaries or verification methods. The counted revision documents a reproducible screened search in OpenAlex plus targeted website and standards searching, with Google Scholar retained only for citation chasing and sensitivity checking; reporting is aligned to PRISMA-ScR and PRISMA-S principles. Results: The evidence shows that operational carbon reduction remains the most immediately measurable pathway through HVAC optimization, envelope improvement, smart energy management systems, and digital measurement, reporting, and verification. However, embodied carbon management through Environmental Product Declarations, material passports, low-carbon procurement, prefabrication, and circularity is necessary to avoid shifting emissions from operation to construction. Contribution: The review develops a four-layer digital life-cycle carbon governance mechanism linking life-cycle boundary setting, data capture, verification and assurance, and policy-market conversion. Singapore pathway: Singapore’s five-phase pathway is repositioned as an operational carbon MRV entry point that must be expanded to whole-life carbon through embodied carbon datasets, EPD-based procurement, and ASEAN-specific localization. The revised pathway identifies implementation risks, including data governance, additionality, double-counting, auditor capacity, SME access, market liquidity, and cross-country transferability. Conclusions: Digital MRV and carbon-market mechanisms can accelerate building decarbonization only when they are coupled with whole-life carbon boundaries, embodied carbon safeguards, transparent review methods, and context-specific financing. Full article

A central premise of climate adaptation is that better information and smarter decisions can keep escalating hazards within manageable bounds. We test this premise for one of the most information-sensitive decisions in disaster management—ordering a hurricane evacuation—and find that it has limits. Taking Hurricane Irma (2017), the storm behind Florida’s largest evacuation (6.5 million people, 4 million vehicles), as a reference event, we add Coupled Model Intercomparison Project Phase 6 (CMIP6) perturbations to the historical storm and use the Pangu-Weather artificial intelligence (AI) forecasting system to generate 20,000 ensemble members for present-day and future climates (Shared Socioeconomic Pathway (SSP) 2-4.5 and SSP5-8.5; 2050s and 2080s). As the climate warms, storm intensity rises by 15–20% and forecast uncertainty roughly doubles. A reinforcement learning (RL) framework that optimizes evacuation orders under these conditions then exposes a paradox: although RL’s advantage over fixed policies grows from 7% today to 17% under the 2080s SSP5-8.5, absolute evacuation performance still deteriorates by 44% despite optimization. The optimized future climate outcome (objective: 0.239) is in fact worse than that of suboptimal fixed policies today (0.178)—better decisions cannot compensate for a decision environment that has itself degraded. This is direct, scenario-specific evidence that optimization-based adaptation has a ceiling, with consequences for the long-term sustainability of hazard-exposed coastal regions: keeping such communities safe and livable will require coupling evacuation optimization with structural risk reduction, equitable access to decision-support technology, and aggressive greenhouse gas mitigation that holds future risk within adaptable—and therefore sustainable—bounds. The framework supplies quantitative support for sustainable disaster risk reduction and resilient infrastructure planning aligned with global sustainability goals. Full article

Restaurants combine long opening hours, catering demand, kitchen ventilation, DHW, and mixed-fuel cooking loads, making their decarbonisation different from generic commercial retrofit. For small- and medium-sized enterprise (SME) hospitality premises, this makes the transition to net-zero operation a distinct sustainability challenge because a large, process-driven share of demand lies outside conventional building-fabric and building-services retrofit. This single-case study develops a whole-building utility-calibrated OpenStudio/EnergyPlus model for Beit El Zaytoun, a 655.82 m² restaurant in Park Royal, London. Monthly electricity and gas data for June 2024–May 2025 were used to calibrate the baseline at whole-building level. Standalone and cumulative scenarios tested insulation, low-emissivity double glazing, LED lighting and controls, ASHP service scenarios, and an 11 kWp PV array. Baseline demand was 413,895 kWh/yr, equivalent to 631.1 kWh/m²·yr and 75,020 kgCO₂e/yr. The lowest-net-energy analytical package reduced net imported energy to 314,734 kWh/yr and operational carbon to 56,700 kgCO₂e/yr, a retained 24.0% reduction on the source reporting basis; this package is treated as an analytical bound rather than as a final design recommendation because it excludes cooling. The model-derived residual process load, kitchen and catering gas plus kitchen, and back-of-house electricity remained 233,920 kWh/yr across building-focused scenarios. The Residual-Load Index (RLI) rose from 0.57 to 0.74; with ±15% process-load allocation uncertainty, the optimised RLI range was 0.63–0.85, so the post-retrofit balance remained process-load dominated. The case demonstrates a practical decarbonisation ceiling likely to recur in similar high-process-load hospitality premises: fabric, lighting, heat electrification, and PV are necessary but insufficient without catering-equipment, cooking-fuel, kitchen-ventilation, refrigeration-control, sub-metering, and demand-response strategies. The paper contributes whole-building utility-calibrated quantitative evidence and a transferable RLI metric for sub-sector-specific sustainable retrofit policy, and the net-zero transition of SME food-service premises. Full article

The transition to electric bus (EB) fleets is a critical step towards sustainable urban transportation, offering substantial reductions in greenhouse gas and pollutant emissions relative to diesel buses. However, transit authorities face multifaceted challenges in this transition, including limited driving ranges of EBs, the need for widespread charging infrastructure, and potential strain on the electric grid, alongside opportunities such as governmental subsidies and increased fare revenues. This paper proposes a comprehensive multi-period mixed-integer programming model seeking to optimize long-term EB fleet transition plans in urban contexts while jointly accounting for all inherent financial, technical, and operational factors impacting such a transition. The model is operationalized using real data acquired from Dubai’s Roads & Transport Authority (RTA), encompassing 71 bus routes and a 25-year planning horizon to meet a 100% electrification target by 2050. A scenario-based analysis evaluates the robustness of the transition plans under variations in key operational parameters. The results illustrate that optimized long-term planning yields substantial cost savings and emissions reductions, where the incorporation of environmental and social externalities and revenue shifts causes profit maximization to emerge as a more appropriate objective. In addition, it turns out that adequate dwell time is crucial for cost containment and full fleet electrification feasibility. While RTA targets 100% electrification by 2050, the base case is deliberately relaxed to 90% as certain routes, notably double-decker lines, are incompatible with currently available EB configurations. Nevertheless, full electrification is restored under the minimum dwell scenario. Also, a policy of purchasing only EBs accelerates full fleet electrification by roughly a decade with only a marginal increase in total cost, unlike imposing strict interim electrification targets. The optimized transition plans provide actionable insights for transit authorities balancing economic efficiency with sustainability goals. Full article

As the core driving force behind the new wave of technological revolution and industrial transformation, digital–green synergy (DGS) has become a crucial pathway of low-carbon development in the tourism industry. On the basis of panel data from 54 coastal cities in China from 2011 to 2023, this study employs baseline regression models, moderation effect models, threshold effect models, and spatial spillover effect models to empirically examine the impact mechanisms of DGS on tourism carbon emission efficiency (TCEE), and its spatial spillover effects. The results indicate that (1) DGS can effectively enhance TCEE. (2) Environmental regulation (ER) and tourism industry agglomeration (TIA) play positive moderating roles in the relationship between DGS and TCEE. (3) The effect of DGS on TCEE exhibits nonlinearity, with a double-threshold characteristic, which leads to leap-like changes. (4) DGS has spatial spillover effects on TCEE, facilitating coordinated emission reductions across regions. (5) The results of the heterogeneity analysis indicate that the promoting effect of DGS on TCEE is more pronounced in the southern marine economic circles and economically advanced regions. The present study offers theoretical evidence and policy insights for promoting the deep integration of digitalization and greening development and for achieving high-quality development of the tourism industry in Chinese coastal regions. Full article

The integration of distributed energy resources (DERs) has enhanced the operational flexibility and complexity of smart building energy management, which is crucial to urban sustainable development. However, the limitations of strategy applicability across different environments and lengthy development cycles pose significant challenges for energy management. To address this, this paper proposes a transferred multi-thread deep reinforcement learning (TMDRL) framework for the cross-domain energy management of smart buildings. Firstly, a source-domain heterogeneous exploration architecture based on multi-thread deep reinforcement learning (DRL) is proposed. A transferable source-domain knowledge base is constructed to enhance the generalization ability of pre-trained strategies. Secondly, a decoupled double-critic optimization mechanism is designed to mitigate policy evaluation bias during cross-domain transfer. Finally, simulations using real-world datasets from different times and areas are conducted. The results show that compared to A3C, DDPG, and SAC, the proposed TMDRL framework reduces total costs by 32.77%, 18.14%, and 37.24%, while improving convergence efficiency by 29.55%, 22.89%, and 32.84%, respectively. The reduction in total cost and improvement in convergence efficiency demonstrate that the proposed TMDRL framework effectively saves energy and enhances the utilization of renewable energy, proving the sustainable benefits of smart building energy management across domains. Full article