Search Results (209)

Traditional Pavement Condition Index (PCI) assessments are highly resource-intensive, demanding substantial time and labor while generating significant carbon emissions through extensive field operations. To address these sustainability challenges, this research presents an innovative methodology utilizing Gene Expression Programming (GEP) to determine PCI values based on International Roughness Index (IRI) measurements from Iraqi road networks, offering an environmentally conscious and resource-efficient approach to pavement management. The study incorporated 401 samples of IRI and PCI data through comprehensive visual inspection procedures. The developed GEP model exhibited exceptional predictive performance, with coefficient of determination (R²) values achieving 0.821 for training, 0.858 for validation, and 0.8233 overall, successfully accounting for approximately 82–85% of PCI variance. Prediction accuracy remained robust with Mean Absolute Error (MAE) values of 12–13 units and Root Mean Square Error (RMSE) values of 11.209 and 11.00 for training and validation sets, respectively. The lower validation RMSE suggests effective generalization without overfitting. Strong correlations between predicted and measured values exceeded 0.90, with acceptable relative absolute error values ranging from 0.403 to 0.387, confirming model effectiveness. Comparative analysis reveals GEP outperforms alternative regression methods in generalization capacity, particularly in real-world applications. This sustainable methodology represents a cost-effective alternative to conventional PCI evaluation, significantly reducing environmental impact through decreased field operations, lower fuel consumption, and minimized traffic disruption. By streamlining pavement management while maintaining assessment reliability and accuracy, this approach supports environmentally responsible transportation systems and aligns contemporary sustainability goals in infrastructure management. Full article

The significant energy intensity of high-speed railway necessitates integrating renewable technologies to enhance grid resilience and decarbonize transport. This study establishes a coordinated carbon–green certificate market mechanism for railway power systems and develops a tri-source planning model (grid/solar/energy storage) that comprehensively considers the full lifecycle carbon emissions of these assets while minimizing lifecycle costs and CO₂ emissions. The proposed EDMOA algorithm optimizes storage configurations across multiple operational climatic regimes. Benchmark analysis demonstrates superior economic–environmental synergy, achieving a 23.90% cost reduction (USD 923,152 annual savings) and 24.02% lower emissions (693,452.5 kg CO₂ reduction) versus conventional systems. These results validate the synergistic integration of hybrid power systems with the carbon–green certificate market mechanism as a quantifiable pathway towards decarbonization in rail infrastructure. Full article

As an energy-intensive industry, the aviation sector’s carbon emissions have drawn significant attention. Against the backdrop of the “dual carbon” goals, how to enhance the carbon emission efficiency of airlines has become an urgent issue to be addressed for both industry development and low-carbon targets. This paper constructs an evaluation system for the carbon emission efficiency of airlines and uses the SBM-DDF model under the global production possibility set, combined with the bootstrap-DEA method, to calculate the efficiency values. On this basis, the fuzzy-set qualitative comparative analysis method is employed to analyze the synergistic effects of multiple influencing factors in three dimensions: economic benefits, transportation benefits, and energy consumption on improving carbon emission efficiency. The research findings reveal that, first, a single influencing factor does not constitute a necessary condition for achieving high carbon emission efficiency; second, there are four combinations that enhance carbon emission efficiency: “load volume-driven type”, “scale revenue-driven type”, “high ticket price + technology-driven type”, and “passenger and cargo synergy mixed type”. These discoveries are of great significance for promoting the construction of a carbon emission efficiency system by Chinese airlines and achieving high-quality development in the aviation industry. Full article

The maritime sector faces urgent decarbonisation pressures due to regulatory instruments, such as the International Maritime Organization’s (IMO) Carbon Intensity Indicator (CII), which mandates reductions in greenhouse gas emissions per transport work. This paper investigates the challenge of identifying CII-compliant strategies that are also financially viable for UK shipowners. To address this, operational and technical data from UK-flagged vessels over 5000 GT are analysed using a capital budgeting framework. This includes scenario-based evaluation of speed reduction, payload limitation, and retrofitting with dual-fuel LNG and methanol engines. The analysis integrates carbon taxation, and pilot fuel use to assess impacts on emissions and profitability. The findings reveal that while the short-term operational measures examined offer modest gains, long-term compliance and financial performance are best achieved through targeted retrofitting supported by carbon taxes and favourable market conditions. The study provides actionable insights for shipowners and policymakers seeking to align commercial viability with regulatory obligations under the evolving CII framework. Full article

Cities house more than half of the world’s population and are responsible for more than 70% of the world anthropogenic CO₂ emissions. Therefore, quantifications of emissions from major cities, which are only less than a hundred intense emitting spots across the globe, should allow us to monitor changes in global fossil fuel CO₂ emissions in an independent, objective way. The study adopted a high-spatiotemporal-resolution regional assimilation method using satellite observation data and atmospheric transport model WRF-Chem/DART to assimilate CO₂ concentration and fluxes in Lisbon, a major city in Portugal. It is based on Zhang’s assimilation method, combined OCO-2 XCO₂ retrieval data, ODIAC 1 km anthropogenic CO₂ emissions and Ensemble Adjustment Kalman Filter Assimilation. By employing three two-way nested domains in WRF-Chem, we refined the spatial resolution of the CO₂ concentrations and fluxes over Lisbon to 3 km. The spatiotemporal distribution characteristics and main driving factors of CO₂ concentrations and fluxes in Lisbon and its surrounding cities and countries were analyzed in March 2020, during the period affected by COVID-19 pandemic. The results showed that the monthly average CO₂ and XCO₂ concentrations in Lisbon were 420.66 ppm and 413.88 ppm, respectively, and the total flux was 0.50 Tg CO₂. From a wider perspective, the findings provide a scientific foundation for urban carbon emission management and policy-making. Full article

Amidst the global strategic transition towards low-carbon energy systems, electric vehicles (EVs) are pivotal for achieving deep decarbonization within the transportation sector. Consequently, enhancing the scientific rigor and precision of their life-cycle carbon footprint assessments is of paramount importance. Addressing the limitations of existing research, notably ambiguous assessment boundaries and the omission of dynamic coupling characteristics, this study develops a dynamic regional-level life-cycle carbon footprint assessment model for EVs that incorporates time-variant carbon emission factors. The methodology first delineates system boundaries based on established life-cycle assessment (LCA) principles, establishing a comprehensive analytical framework encompassing power battery production, vehicle manufacturing, operational use, and end-of-life recycling. Subsequently, inventory analysis is employed to model carbon emissions during the production and recycling phases. Crucially, for the operational phase, we introduce a novel source–load synergistic optimization approach integrating dynamic carbon intensity tracking. This is achieved by formulating a low-carbon dispatch model that accounts for power grid security constraints and the spatiotemporal distribution of EVs, thereby enabling the calculation of dynamic nodal carbon intensities and consequential EV emissions. Finally, data from these distinct stages are integrated to construct a holistic life-cycle carbon accounting system. Our results, based on a typical regional grid scenario, reveal that indirect carbon emissions during the operational phase contribute 75.1% of the total life-cycle emissions, substantially outweighing contributions from production (23.4%) and recycling (1.5%). This underscores the significant carbon mitigation leverage of the use phase and validates the efficacy of our dynamic carbon intensity model in improving the accuracy of regional-level EV carbon accounting. Full article

This paper investigates the optimal planning of high-speed rail (HSR) freight operations, pricing strategies, and government carbon tax policies. The primary objective is to enhance the market share of HSR freight, thereby reducing carbon dioxide (CO₂) emissions associated with freight activities. The modal shift problem is formulated as a bi-level multi-objective model and solved using a specifically designed hybrid algorithm. The upper-level model integrates multiple objectives of the government (minimizing tax while maximizing the emission reduction rate) and HSR operators (maximizing profits). The lower-level model represents shippers’ transportation mode choices through network equilibrium modeling, aiming to minimize their costs. Numerical analysis is conducted using a transportation network that includes seven major central cities in China. The results indicate that optimizing HSR freight services with carbon tax policies can achieve a 56.97% reduction in CO₂ emissions compared to air freight only. The effectiveness of the government’s carbon tax policy in reducing CO₂ emissions depends on shippers’ emphasis on carbon reduction and the intensity of the carbon tax. Full article

Facing the increasingly severe challenges of global climate change, China has established clear “dual carbon” goals, with the core objective of achieving carbon peak by 2030 or earlier. However, carbon emissions from the road freight industry have remained higher for many years; understanding and estimating the characteristics of truck carbon emissions are critical for developing a low-carbon transportation system. This study takes Heilongjiang Province, a typically cold region, as a case study. By employing the growth curve method, we predicted the time for achieving carbon peak and constructed an improved STIRPAT model to identify key drivers and pathways for emission reduction in the road freight system. The research results show that only by committing to using the economy to reduce carbon emissions and improve energy intensity can the overall carbon emissions of Heilongjiang Province’s cargo transportation system achieve the “dual carbon” goals as soon as possible. If we develop according to the optimistic scenario proposed in this article, by 2030, the total quantity of trucks will reach about 933,720, and the carbon emissions per vehicle will reach about 178.14 t. If we actively increase the proportion of new energy trucks in the overall quantity of trucks, the peak time is expected to be achieved around 2030. The improvement of technological efficiency (e.g., lowering energy intensity) and the advancement of economic development have been identified as effective pathways for carbon emission reduction. Empirical studies indicate that these measures can achieve emission reduction impacts that are approximately 60 times and 10 times greater, respectively, in terms of efficiency, compared to baseline scenarios. Furthermore, energy intensity improvements and structural shifts toward low-carbon vehicles are critical to expediting peak attainment. This study provides a methodological framework for cold-region emission projections and offers actionable insights for policymakers to design tailored emission reduction pathways in the road freight transportation industry. Full article

Heilongjiang Province, as China’s principal gateway for Russian timber imports, faces structural inefficiencies in the localization of wood-processing enterprises—characterized by ecological sensitivity, resource–industry mismatches, and uneven spatial distribution. To address these challenges, this study proposes a two-stage site selection framework that integrates Geographic Information Systems (GIS) with an enhanced Non-dominated Sorting Genetic Algorithm II (NSGA-II). The model aims to reconcile ecological protection with industrial efficiency by identifying optimal facility locations that minimize environmental impact, reduce construction and logistics costs, and enhance service coverage. Using spatially resolved multi-source datasets—including forest resource distribution, transportation networks, ecological redlines, and socioeconomic indicators—the GIS-based suitability analysis (Stage I) identified 16 candidate zones. Subsequently, a multi-objective optimization model (Stage II) was applied to minimize carbon intensity and cost while maximizing service accessibility. The improved NSGA-II algorithm achieved convergence within 700 iterations, generating 124 Pareto-optimal solutions and enabling a 23.7% reduction in transport-related CO₂ emissions. Beyond carbon mitigation, the model spatializes policy constraints and economic trade-offs into actionable infrastructure plans, contributing to regional sustainability goals and transboundary industrial coordination with Russia. It further demonstrates methodological generalizability for siting logistics-intensive and policy-sensitive facilities in other forestry-based economies. While the model does not yet account for temporal dynamics or agent behaviors, it provides a robust foundation for informed planning under China’s dual-carbon strategy and offers replicable insights for the global forest products supply chain. Full article

This article summarizes the scope and content of the Special Issue (SI) entitled “Geographic Information Systems (GIS) and Cartography for a Sustainable World” and its contribution to the global discourse regarding sustainability concerns. At the heart of the discussion in this SI lies: (i) GIS, a valuable tool and a means for modeling, designing, and analyzing (spatial) data and processes related to the pursuance of sustainability objectives at both local and global scales; and (ii) Cartography as a discipline, which through maps and visualizations can convey the present state. The latter can play a vital role in educating, empowering, and raising public awareness with regard to sustainability concerns on the one hand, and can form a basis for policy-makers, scientists, and citizens for articulating effective sustainability strategies on the other. The fulfillment of the SI goals is attained through a collection of 26 papers that delve into and attempt to visualize sustainability achievements or concerns on a variety of themes in different parts of the world. More specifically, the content of this collection of papers can be categorized into the following sustainability-related themes: Urbanization, Transportation, Carbon Emissions Management, Infrastructure, Rural Development, and Climate Change. The main conclusion is that planning and implementing sustainability policies is a challenging and multi-level task, and must be carried out within a fully dynamic decision environment. Although some progress has already been made, more intensive and collective efforts from scientists, governments, the entrepreneurial community, and citizens are needed in order for the ambitious goals of Agenda 2030 to be reached. Full article

Aviation is widely recognised as one of the most carbon-intensive modes of transport and among the most challenging sectors to decarbonise. The use of green hydrogen (H₂) in airside operations can help reduce emissions from air transport. While the pace and scalability of technology development, including H₂-powered and ground support equipment, will be key factors, other financial, regulatory, legal, organisational, behavioural, and societal issues must also be considered. This paper investigates the key opportunities and challenges of using H₂ in the aviation industry through eleven semi-structured interviews and a virtual expert workshop (N = 37) with key aviation industry stakeholders and academia. The results indicate that, currently, decarbonisation of the aviation sector faces several challenges, including socio-technical, techno-economic, and socio-political challenges, with socio-technical challenges being the most prominent barrier. This study shows that decarbonisation will not occur until the UK government is ready to have all the required infrastructure and capacity in place. Governments can play a significant role in directing the necessary ‘push’ and ‘pull’ to develop and promote zero-carbon emission aircraft in the marketplace and ensure safe implementation. Full article

Concrete and other cementitious materials are among the most widely used construction materials worldwide. However, their high embodied carbon emissions and energy-intensive manufacturing processes pose significant environmental challenges. This study assesses the carbon emissions, cost implications, and circularity potential of a novel concrete mix, Tex-crete, which incorporates recycled textile and cardboard fibres as sustainable alternatives to conventional reinforcement and cementitious materials in concrete. The study employs a cradle-to-gate life cycle assessment (LCA) approach to compare carbon emissions and costs across different mix designs, using two case studies: a temporary construction site compound and a footpath. Experimental results indicate that Tex-crete, particularly the KFT mix design (including 2.5% textile fibres with treated kraft fibres), achieves comparable compressive and tensile strength to traditional concrete while demonstrating a net reduction in both carbon emissions (3.38%) and production costs (2.56%). A newly introduced circularity index (CI) further evaluated the reuse, repair, and recycling potential of the novel mix, revealing that KFT exhibits the highest circularity score (0.44). Parametric analysis using Monte Carlo simulations highlighted transportation distance and energy consumption during fibre processing as key factors influencing emissions. The findings provide valuable insights for industry stakeholders seeking sustainable concrete solutions aligned with circular economy principles, offering an optimized balance between environmental performance, structural integrity, and cost-effectiveness. Full article

Promoting carbon emission reduction in road freight transportation is important to achieve low-carbon development. The carbon trading mechanism is an effective market mechanism to promote carbon emission reduction. The digital and networked features of the online freight platform (OFP) service supply chain (SSC) not only help the platform reduce carbon emissions but also facilitate the government’s achievement of efficient and economic supervision of carbon emissions. Therefore, this paper proposes two types of carbon trading mechanism based on the OFP SSC to investigate the carbon emission reduction decision of the OFP, namely an absolute emission cap-based allocation (AC) model and an intensity-based allocation (IC) model. By using game theory, we then analyze the optimal solutions of the OFP SSC under the non-participation in carbon trading market (NC model), the AC model, and the IC model. By comparing these decisions, we explore the impact of the carbon trading mechanism on the OFP SSC. Results show the following: (1) Carbon trading mechanisms reduce OFP emissions, particularly under IC models with high free allowances. (2) High initial allowances and low service costs under the carbon trading mechanism enhance the OFP’s profit. (3) The carbon trading mechanism can reduce the carbon emissions of the road freight sector when initial allowances are sufficient or the off-platform trucker’s carbon emission coefficient is low. The study concludes that the IC model optimizes emission cuts while maintaining platform profitability. From a managerial perspective, the government should adopt dynamic allowance policies and incentivize the OFP’s participation through data integration. OFPs must balance network growth with low-carbon technology adoption to align commercial and environmental objectives. Full article

Amid intensified global climate mitigation efforts, integrating rail freight into carbon emissions trading schemes became critical under China’s “Dual-Carbon” strategy. Despite rail’s significantly lower emissions intensity compared to road transport, existing pricing frameworks inadequately internalized its environmental externalities, which limited its competitive advantage. To address this gap, this study systematically reviewed international and domestic practices of integrating transport into carbon trading systems and developed a novel “four-layer, three-dimensional” emissions trading scheme (ETS) framework tailored specifically for China’s rail freight sector. Employing a Stackelberg bilevel optimization model, this study analyzed how carbon quotas and pricing influenced rail operators’ pricing and investment decisions. The results showed that under optimized quotas and carbon prices, railway enterprises were able to generate surplus carbon credits, creating new revenue streams and enabling freight rate reductions. This “carbon revenue–freight rate feedback loop” not only delivered environmental benefits but also enhanced rail’s economic competitiveness. Overall, this study significantly advances the understanding of carbon-based pricing mechanisms in railway freight, providing robust theoretical insights and actionable policy guidance for achieving sustainable decarbonization in China’s transport sector. Full article

Transportation infrastructure systems sit at the nexus of urban economic development and emission mitigation. The primary objective is to identify and quantify the key factors influencing CI, with a focus on both the conventional and emerging indicators through an innovative MLP neural network developed using the data of 20 Chinese transportation enterprises that have a business focus on the construction and operation sector from 2018 to 2022. The hypothesis is that integrating unconventional indicators—such as business model entropy and green revenue share—alongside traditional metrics can significantly enhance the predictive accuracy for CI. The results show that business model entropy explains 42.6% of carbon intensity (Cl) variability through green revenue diversification pathways, while emissions trading system (ETS) exposure accounts for 51.83% of decarbonization outcomes via price-signaling effects. The analysis reveals that a critical operational threshold–renewable energy capacity below 75% fails to significantly reduce Cl, and capex/revenue ratios exceeding 73.58% indicate carbon lock-in risks. These findings enable policymakers to prioritize industries with sub-75% renewable adoption while targeting capex-intensive sectors for circular economy interventions. The novelty of this work lies in the application of advanced machine-learning techniques to a comprehensive, multi-source dataset, enabling a nuanced analysis of CI drivers and offering actionable insights for policymakers and industry stakeholders aiming to decarbonize transport infrastructure. Full article