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To reach carbon neutrality, renewable energy development will gradually accelerate. Tidal current energy, with high energy density, long-term predictability and potentially large reserves, is an important research direction. As the prerequisite for tidal current energy development, Tidal Current Power Plant Site Selection (TCPPSS) affects its future benefits. Although some studies have been conducted on TCPPSS, further work is still needed on criteria system, research scales and methods. In this paper, a three-stage decision framework is proposed based on Geographic Information System (GIS) and Multi-Criteria Decision Making (MCDM) to determine potential marine areas, extract and evaluate site alternatives. In this framework, a scientific criteria system with exclusion and evaluation criteria is established. To reduce subjective deviations in Analytic Hierarchy Process (AHP), a combinative weighting method based on Fuzzy Group decision making AHP (FGAHP) and CRiteria Importance Through Intercriteria Correlation (CRITIC) is developed to calculate the criteria weights. Considering the maximum group utility and minimum individual regret, VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), a compromise method, is used to rank the alternatives. The proposed framework is applied to a case of Shandong Province, Eastern China. The results are verified through sensitivity analysis and can provide a direct reference for decision-makers. • A three-stage decision framework for Tidal Current Power Plant Site Selection is proposed. • The framework based on a novel method includes both large-scale site selection and small-scale site selection. • A scientific criteria system including exclusion and evaluation criteria is established. • A combinative weighting method is developed to calculate criteria weights. • A case study is carried out in Shandong Province, Eastern China. [ABSTRACT FROM AUTHOR]

The expansion of road networks in emerging economies such as China causes significant greenhouse gas (GHG) emissions. This development is conflicting with China's commitment to achieve carbon neutrality. Thus, there is a need to better understand life cycle emissions of road infrastructure and opportunities to mitigate these emissions. Existing impact studies of roads in developing countries do not address recycled materials, improved pavement maintenance, or pavement-vehicle interaction and electric vehicle (EV) adoption. Combining firsthand information from Chinese road construction engineers with publicly available data, this paper estimates a comprehensive account of GHG emissions of the road pavement network to be constructed in the next ten years in the Shandong province in Northern China. Further, we estimate the potential of GHG emission reductions achievable under three scenario sets: maintenance optimization, alternative pavement material replacement, and EV adoption. Results show that the life cycle GHG emissions of highways and Class 1–4 roads to be constructed in the next 10 years amount to 147 Mt CO2-eq. Considering the use phase in our model reveals that it is the dominant stage in terms of emissions, largely due to pavement-vehicle interaction. Vehicle electrification can only moderately mitigate these emissions. Other stages, such as materials production and road maintenance and rehabilitation, contribute substantially to GHG emissions as well, highlighting the importance of optimizing the management of these stages. Surprisingly, longer, not shorter maintenance intervals, yield significant emission reductions. Another counter-intuitive finding is that thicker and more material-intensive pavement surfaces cause lower emissions overall. Taken together, optimal maintenance and rehabilitation schedules, alternative material use, and vehicle electrification provide GHG reduction potentials of 11%, 4%–16% and 2%–6%, respectively. • Road construction implies significant amounts of GHG emissions. • China's road expansion plans are at odds with its target of GHG neutrality. • We assess GHG emissions of Shandong's roads to be constructed through 2030. • We combine life cycle assessment based on primary data with a new optimization model. • Individual mitigation measures can reduce life cycle GHG emissions by up to 16%. [ABSTRACT FROM AUTHOR]