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With the continuous development of global economic and trade activities, environmental problems have become an important factor restricting the sustainable development of all countries. How to realize the coordinated development of international trade and environmental protection has become a major issue facing the international community. Since China joined the WTO, its share of international trade has been increasing continuously. In order to deeply analyze the influence of international carbon emission trading policy on domestic carbon emissions, we use an input–output model and a GTAP analysis method to theoretically calculate the carbon emissions of the international trade of various departments in Shandong Province. At the same time, the implicit carbon emission index of various industries in 2022 is calculated through the direct energy consumption coefficient. The results show that there are significant differences in the impact of the carbon tariff system on different industries. In terms of the carbon emission index, the food processing industry showed a decrease of 18.99 Mt, while the implied carbon emission of the tobacco, textile and leather manufacturing industry reached 30.56 Mt due to the continuous expansion of trade scale. In contrast, the implied carbon emission level of the metal product processing industry reached 5.3 Mt, while the carbon emission of traditional trading industries such as coal mining was almost unaffected by international trade, and its carbon emission index reached the highest level of 5.89 in 2020. In terms of trade impact, high-trade industries such as the food processing industry are significantly affected by the carbon tariff policy, and their share has dropped from 5.89% to 3.95% in the past decade. The carbon emissions generated by GDP growth established by the GTAP model are more convincing. This model can directly reflect the energy efficiency of a region from the side. Based on the present situation of international trade, this paper analyzes the inequality of the current carbon tariff system, and puts forward some policies to optimize the energy structure to reduce carbon emissions and expand domestic demand to reduce the dependence on international trade. Through the GTAP model, we put forward policy suggestions to optimize the energy structure to reduce carbon emissions and the dependence on international trade by expanding domestic demand. [ABSTRACT FROM AUTHOR]

Shallow geothermal energy (SGE) is a renewable energy source that has the advantage of being low-cost, clean, and locally sourced compared to fossil fuels, and is thus significant for China to achieve its future goals of carbon peaking and carbon neutrality. However, determining how to improve the operational efficiency of SGE systems is a key factor in the sustainable development and utilization of geothermal energy. This study examined the long-term operational efficiency of SGE systems and applied numerical simulation methods of hydro-thermal coupling to a SGE utilization project in Shandong Province, China. The effect of the distribution of pumping and injection wells on the operation efficiency of the SGE system was analyzed, and the parameter of operation efficiency, defined by the ratio of the practical minable shallow geothermal energy to the theoretical shallow geothermal energy, was applied to quantify the operation efficiency of the SGE system. The simulated results show that the phenomenon of heat transfixion is significant in the current operation scheme, where one of three pumping wells is located downstream of the study area, which indicates that the local groundwater flow field mainly controls the operation efficiency of the SGE system. In the optimized operation scheme, the distribution of pumping and injection wells can be adjusted according to the feature of groundwater flow and temperature fields. The degree of heat transfixion significantly declines and the operation efficiency increases by 71.5%. In addition, further improvements in the operational efficiency of the SGE system can be considered through the running time. The findings of this paper will be useful for the construction and management of SGE systems. [ABSTRACT FROM AUTHOR]

Land cover change affects the carbon emissions of ecosystems in some way. The qualitative and quantitative understanding of carbon emissions from human activities (e.g., land cover change, industrial production, etc.) is highly significant for realizing the objective of carbon neutrality. Therefore, this paper used GlobeLand30 land cover maps, annual average normalised difference vegetation index (NDVI) data, annual average net ecosystem productivity (NEP) data and statistical yearbook data from 2000 to 2020 to explore the relationship between land cover change and carbon emissions. Specifically, it included land cover change, carbon storage changes influenced by land cover change, spatial and temporal analysis of carbon sources and sinks, land use intensity change and anthropogenic carbon emissions. The results of the study show that the main land cover changes in Shandong province during 2000–2020 was cultivated land conversion to artificial surfaces. Among them, the area of cultivated land converted to artificial surfaces from 2000 to 2010 was 4930.62 km2, and the proportion of cultivated land converted to artificial surfaces from 2010 to 2020 was as high as 78.35%. The total carbon stock of vegetation affected by land cover change decreased by 463.96 × 104 t and 193.50 × 104 t in 2000–2010 and 2010–2020 respectively. The spatial and temporal distribution of carbon sources and sinks differed more markedly from 2000 to 2020, and land use intensity changes in Shandong Province showed an upward trend. Of the total energy production, industry has the largest energy consumption, followed closely by total energy consumption in transportation, storage and postal services. [ABSTRACT FROM AUTHOR]

Against the background of 'Emission Peak, Carbon Neutrality', there is a significant integration of new energy into the power grid. The characteristics of 'double high' and 'double peak' in power systems become more pronounced, posing a substantial challenge to the stability and energy coordination of the power grid. To address this, we propose a flexible interconnection scheme based on an AC-DC bidirectional converter. It can solve the problem of high-load caused by new energy generation. Firstly, high-load and adjacent substations are flexibly interconnected through the DC bus on the low-voltage side, enabling load management within a small range. Secondly, AC-DC power transfers the excess new energy generation to other substations. It can promote the absorption of new energy. This method can improve the utilization efficiency of distribution transformers, realize capacity sharing between different transformers, and improve the reliability of power systems in low-voltage distribution networks. Finally, based on a DC interconnection project in Shandong Province (China), the effectiveness of the proposed flexible interconnection scheme is verified. The new energy consumption increases from 5% to 24.4%, and the load rate of high-load transforms drops below 70%. [ABSTRACT FROM AUTHOR]

The ocean is a key component of the global carbon cycle. As climate change becomes increasingly severe, enhancing the ocean's carbon sink capacity is of great significance to achieving the "dual carbon" goal for China. As a major maritime province, Shandong is in an advantageous position to utilize the ocean to respond to climate change and achieve the national goals of carbon peaking and carbon neutrality. Focusing on consolidating and enhancing ocean carbon sink capabilities, Shandong needs to formulate a clear timetable and roadmap to promote blue carbon technology research and development, actively cultivate a blue carbon trading market, and tap the inner potential of ocean carbon sink. Shandong should also strengthen international cooperation to jointly construct a carbon-neutral global community. [ABSTRACT FROM AUTHOR]

Concerning the vision of achieving carbon neutral and peak carbon goals, wind energy is extremely important as a renewable and clean energy source. However, existing research ignores the implicit features of the data preprocessing technique and the role of the internal mechanism of the optimization algorithm, making it difficult to achieve high-accuracy prediction. To fill this gap, this study proposes a wind speed forecasting model that combines data denoising techniques, optimization algorithms, and machine learning algorithms. The model discusses the important parameters in the data decomposition technique, determines the best parameter values by comparing the model's performance, and then decomposes and reconstructs the wind speed time series. In addition, a novel optimization algorithm is used to optimize the parameters of the machine learning algorithm using a waiting strategy and an aggressive strategy to improve the effectiveness of the model. Several control experiments were designed and implemented using 10-min wind speed data from three sites in Penglai, Shandong Province. Based on the numerical comparison results and the discussion of the proposed model, it is concluded that the developed model can obtain high accuracy and reliability of wind speed prediction in the short term relative to other comparative models and can have further applications in wind power plants. [ABSTRACT FROM AUTHOR]

In recent decades, large-scale deployment of photovoltaic (PV) power leads to management challenges for recycling PV module waste in China. With the growth of waste PV volumes, it is necessary to figure out the spatio-temporal distribution of PV waste at the provincial level. Based on China's carbon neutrality goal by 2060, six development pathways of PV installed capacity are proposed to identify in-use stocks of PV capacity. In particular, we develop the retired flow estimation model for PV modules that is constructed by three PV module degradation scenarios. The results show that a relatively large scale of PV waste will be started to emerge in China by 2030 and the cumulative waste is expected to reach 1100 ∼ 1450 GW by 2060. Our findings also indicate an unequal distribution of PV waste across regions and the highest PV waste volumes by 2060 is the East China region at 31.4%, with Shandong (8.99%) and Hebei(8.65%) ranking as the top provinces. This prospective research will help the PV industry plan the location and capacity of recovery facilities at an appropriate time to advance toward a more resource efficient and circular economy. [ABSTRACT FROM AUTHOR]

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]

The energy-intensive industrial sector (EIIS) occupies a majority of global CO2 emissions, but spatially monitoring the spatiotemporal dynamics of these emissions remains challenging. In this study, we used the Chinese province with the largest carbon emissions, Shandong Province, as an example to investigate the capacity of remotely sensed thermal anomaly products to identify annual industrial heat source (IHS) patterns at a 1 km resolution and estimated the carbon emissions of these sources using auxiliary datasets and the boosting regression tree (BRT) model. The IHS identification accuracy was evaluated based on two IHS references and further attributed according to corporate inventory data. We followed a bottom-up approach to estimate carbon emissions for each IHS object and conducted model fitting using the explanatory strength of the annual population density, nighttime light (NTL), and relevant thermal characteristic information derived from the Visible Infrared Imaging Radiometer Suite (VIIRS). We generated a time series of IHS distributions from 2012 to 2020 containing a total of over 3700 IHS pixels exhibiting better alignment with the reference data than that obtained in previous work. The results indicated that the identified IHSs mostly belonged to the EIIS, such as energy-related industries (e.g., thermal power plants) and heavy manufacturing industries (e.g., chemistry and cement plants), that primarily use coal and coke as fuel sources. The BRT model exhibited a good performance, explaining 61.9% of the variance in the inventory-based carbon emissions and possessing an index of agreement (IOA) of 0.83, suggesting a feasible goodness of fit of the model when simulating carbon emissions. Explanatory variables such as the population density, thermal power radiation, NTL, and remotely sensed thermal anomaly durations were found to be important factors for improving carbon emissions modeling. The method proposed in this study is useful to aid management agencies and policymakers in tracking the carbon footprint of the EIIS and regulating high-emission corporations to achieve carbon neutrality. [ABSTRACT FROM AUTHOR]