Frameworks for assessing greenhouse gas emissions have been established to quantify, benchmark, and certify the environmental performance of hotels throughout their life cycles. However, these frameworks exhibit significant variations in methodological scope, system boundaries, and practical applicability. As a result, they are often used interchangeably despite serving distinct analytical purposes, leading to inconsistencies in reported emissions and challenges in decision-making, particularly in complex operational contexts such as luxury hotels and in regions where globally harmonized assessment practices are inconsistently represented. This study undertakes a structured, comparative review of hotel-specific GHG frameworks derived from peer-reviewed literature, industry methodologies, and grey literature through systematic searches and targeted screening. The approaches are categorized into three functional types: methodological calculation, benchmarking, and certification, and are compared based on boundary definition, functional units, data requirements, transparency, and suitability for high-end hotel operation. To explore the practical implications of these methodological differences, insights from a representative luxury international chain hotel in Beijing were incorporated as an illustrative case. The analysis revealed trade-offs between analytical completeness, comparability, and feasibility. No single framework fully addresses the operational and contextual needs of high-end hotels in the context of China’s evolving regulations and energy systems. Building on the shared elements identified across frameworks, this study proposes a harmonized conceptual approach to hotel GHG assessments and discusses its implications for application in the Chinese context. These findings contribute to the methodological clarity of hospitality sustainability research and provide guidance for various stakeholders seeking a more transparent and context-appropriate evaluation of hotel-related emissions.
Rapid electric vehicle (EV) adoption is central to decarbonizing road transport, but national averages obscure substantial city-level differences, limiting understanding of the local conditions that shape market diffusion and its carbon implications. We assembled a city-year dataset covering 296 Chinese cities from 2019 to 2024 and compiled 21 candidate predictors spanning climatic, environmental, socioeconomic, urban, energy-price, infrastructure, and market-maturity dimensions. Following variable screening, 13 substantive predictors were analyzed using a random forest model with city-grouped validation. SHapley Additive exPlanations (SHAP), a 300-replicate city-block bootstrap, region-specific models, and two alternative model specifications were used to interpret predictions and assess uncertainty and sensitivity. EV penetration increased from 4.0% in 2019 to 44.9% in 2024, while substantial spatial disparities persisted. The model generalized well to held-out cities, achieving an R2 of 0.902, an RMSE of 0.048, and an MAE of 0.027. EV stock share was the most important substantive predictor, followed by gasoline price and charging infrastructure stock. In the dynamic specification, the one-year-lagged penetration rate became the dominant predictor, indicating that EV stock share primarily captures adoption momentum and path dependence rather than an independently identifiable peer effect. Predictor rankings also varied across regions, particularly for gasoline prices and charging infrastructure. These findings describe predictive associations rather than causal effects, but they highlight the importance of considering local market maturity, energy prices, and infrastructure conditions when investigating region-specific electrification strategies. The carbon implications of these strategies should also be assessed in relation to regional grid carbon intensity and powertrain composition.
Household carbon emissions (HCEs) are an important yet comparatively underexamined area of climate mitigation. This review synthesizes theoretical and empirical evidence on how population dynamics shape HCEs beyond the effects of aggregate population size. It develops a conceptual framework linking four dimensions of population dynamics - household size and structure, population ageing, fertility and childrearing, and migration and urbanization - to direct carbon emissions (DCEs) and indirect carbon emissions (ICEs) through household-scale, life-course, family-formation, and spatial-contextual mechanisms. The empirical evidence shows that the effects of population dynamics are neither uniform nor linear. Household size exhibits a relatively consistent scale effect: larger households generally generate higher total emissions but lower emissions per capita because of resource sharing and economies of scale. By contrast, population ageing operates through competing pathways, reducing emissions through lower mobility and discretionary consumption while potentially increasing residential energy use and healthcare-related emissions. Childrearing similarly expands demand for housing, mobility, food, education, and childcare but may constrain other consumption through financial pressures and precautionary saving. Migration and urbanization reshape emissions by relocating households across different built-environment, transport, infrastructure, and energy-system contexts, with outcomes depending strongly on urban form and development stage. Divergent empirical findings also reflect differences in emission accounting boundaries, data levels, demographic measures, model specifications, and regional contexts. Overall, population dynamics should be understood as context-dependent upstream conditioning processes rather than linear determinants of HCEs. Climate mitigation strategies should therefore move beyond aggregate population indicators and account for heterogeneity in household structure, life-course transitions, and spatial and institutional contexts.
Dynamic Life Cycle Assessment (LCA) ranges in temporal complexity, with fully dynamic approach requiring full life-cycle chronologies for both foreground and background systems. For instance, LCAs based on Environmental Product Declaration (EPDs), required by the French RE2020 regulation, are partially dynamic, since only the foreground timeline is available. The goal of this research is to provide a user-friendly and rigorous method to conduct partially dynamic LCAs for climate change impact based on EPDs. Delay factors are built as coefficient depending on the time distribution of emissions, to define the dynamic characterization factor as a function of the static one. Compatibility constraints between static and dynamic imposes an observation time T equal to the sum of the Life Cycle Duration (LCD) and the Time Horizon of the Impact (THI). Literal mathematical expressions of delay factors and their behaviors are provided for Global Warming Potential (GWP) and Global Temperature Potential (GTP). The application scope of these delay factors covers all greenhouse gases (GHG) and large temporal range of LCD and THI. A case study based on three background products and randomly generated foreground emissions shows that the RE2020 regulation dynamic factors overestimate benefits obtained by delaying emissions compared to present method. This happens because the method ignores compatibility constraints between static and dynamic approaches and because it does not differentiate delay factors for distinct GHGs. However, delaying emissions reduces GWP but still raises GTP, questioning the use of GWP as a dynamic indicator, as it may falsely suggest a declining impact when it is actually increasing.
Regional disparities in greenhouse gas (GHG) emissions are substantial across large and economically diverse territories in Russia, making subnational assessments important for the implementation of national climate policy. As a major industrial and energy-producing region in the Volga Federal District, the Republic of Tatarstan provides a relevant case for examining emission dynamics under sustained economic growth. This study investigates the trends and driving forces of GHG emissions generated from fuel combustion in Tatarstan over the period 2005-2022 by applying the logarithmic mean Divisia index (LMDI) decomposition method in both the temporal and sectoral dimensions. The results show that total GHG emissions from fuel combustion increased from 29.56 Mt in 2005 to 36.07 Mt in 2022, representing an overall growth of 22%. The results of the decomposition analysis identify economic activity as the dominant driver of emission growth, contributing to a cumulative increase of 16.50 Mt. In contrast, improvements in energy intensity served as the primary mitigating factor, offsetting emissions by 10.17 Mt. Overall, these findings confirm that economic activity remained the dominant driver of GHG emission growth in the Republic of Tatarstan throughout the entire study period, while improvements in energy intensity functioned as the primary offsetting factor. These conclusions underscore the importance of complementing existing conventional mitigation measures with additional structural and technological solutions aimed at further reducing the carbon intensity of the economy while maintaining long-term economic growth, and they provide analytical support for developing targeted, region-specific climate strategies in highly industrialized regions of Russia.
