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China is actively heeding the call and striving for a low-carbon and environmentally friendly development route as part of the general trend toward a global low-carbon economy. The rapid economic development of our nation has brought to light the issue of carbon emissions resulting from the consumption habits of residents. This paper delves into the topic by conducting a thorough analysis of the carbon emission of residents' consumption using the input-output method. Based on the survey findings, the disparity in carbon emission between urban and rural communities in Shandong Province is evident not only in terms of total emissions but also in different categories and regions. Urban residents generate a total carbon emission of approximately 70.2921 million tons, which is three times higher than that of rural residents at 23.7846 million tons. The carbon emission of both urban and rural residents is primarily attributed to their embodied carbon emission. In examining the composition of the implied carbon emission of residents' consumption in Shandong Province, urban residents' carbon emission is concentrated in three areas, namely, food, culture, education, entertainment, transportation, and communication, accounting for 21.45%, 20.88%, and 15.10%, respectively. Conversely, rural residents' carbon emission is concentrated in four areas, including clothing, food, culture, education, entertainment, transportation, and communication, accounting for 29.01%, 17.45%, 15.43%, and 13.36%, respectively. Finally, according to the characteristics of the consumption carbon emission of urban and rural residents in Shandong Province, we give corresponding emission reduction strategies. [ABSTRACT FROM AUTHOR]

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]

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To reduce carbon emissions effectively in Shandong province in New Normal economy, this paper presents a new way to reflect the situation of carbon emissions accurately from both time-perspective and spatial perspective. The characteristics of carbon emissions in Shandong province are presented during 2005-2014, and this paper explores the spatial autocorrelation of carbon emissions branches in 17 cities. The results show that the total amount of carbon emissions in Shandong province presented a steady growth, whereas the growth rate was low, and the proportion of coal consumption in energy structure was significantly higher than the national average. In addition, the spatial pattern of the carbon emissions in 17 cities presented obvious regionally distinction. Furthermore, the affecting factors governing carbon emissions are decomposed based on the Kaya identity. The results reveal that economic scale was the key factor to increase the carbon emissions, whereas energy efficiency was the main factor of carbon emissions reduction. Therefore, this paper draws government should promote carbon emissions reduction given priority to some regions virtually, while encouraging emission reduction in others. [ABSTRACT FROM AUTHOR]

Chinese Green Climate Fund (GCF) is expected to launch by the government in the foreseeable future to support local governments in climate change adaption and mitigation. Lessons from experiences of international financing and allocating mechanisms provide valuable references for establishing the Chinese GCF. This study aims to explore the feasibility of several well-recognized schemes for financing and allocating the Chinese GCF among China's 30 provinces. These include: financing schemes of historical emission responsibility (HR), ability-to-pay (AP) and preference score compromises (PSC); as well as allocating schemes of carbon emissions intensity (CI), carbon emissions per capital (CC), GDP per capital (GC) and PSC. This study derives several important findings. Firstly, given different financing schemes, the coastal economically developed provinces including Shandong, Jiangsu, Guangdong, and Zhejiang would collectively shoulder most of the financing responsibility (almost 70% in total) and play central roles in climate financing. Shandong province presents high financing responsibilities mainly due to its export trades of primary products which rely on the development of high-polluted industries. Other three provinces show high financing responsibilities mainly due to the rapid growth of their industries (petrochemical, steel, building materials, electric power, etc.) for economic development. Secondly, the inland economically backward provinces including Shanxi, Ningxia, Guizhou, Xinjiang, and Gansu would be the top five largest recipients of the funds, obtaining more than 30% of total disbursements. The innovations and contributions of the present study are as follows: Firstly, this paper systematically explored the feasibility and alternative schemes for Chinese GCF for the first time. Further, it introduces PSC methods based on diversified approaches to explore suitable financing and allocating schemes. The schemes proposed would establish theoretical foundations for the Chinese GCF and would also promote the development of other kinds of financing for China and countries with similar economic features. [ABSTRACT FROM AUTHOR]

Introduction: Power industry is one of the largest sources of CO2 emissions in China. The Yellow River Basin plays a supportive role in guaranteeing the effective supply of electricity nationwide, with numerous power generation bases. Understanding the drivers and peak of CO2 emissions of power industry in the Yellow River Basin is vital for China to fulfill its commitment to reach carbon emissions peak by 2030. Methods: The Logarithmic Mean Divisia Index (LMDI) model was employed to explore the drivers to the change of CO2 emissions in power industries of three study areas, including Inner Mongolia Autonomous Regions, Shanxi Province, and Shandong Province in the Yellow River Basin. And Back Propagation (BP) neural network was combined with scenario analysis to empirically predict the trend of the amount of CO2 emitted by power industry (CEPI) from provincial perspective. Results: CEPI in Inner Mongolia under the scenarios of a low degree of CO2 emissions promotion with a medium degree of CO2 emissions inhibition (LM) and a low degree of CO2 emissions promotion with a high degree of CO2 emissions inhibition (LH) scenario can reach a peak as early as 2030, with the peak value of 628.32 and 638.12 million tonnes, respectively. Moreover, in Shanxi, only CEPI under a low degree of CO2 emissions promotion scenarios (LL, LM, LH) can achieve the peak in 2025 ahead of schedule, with amounts of 319.32, 308.07, and 292.45 million tonnes. Regarding Shandong, CEPI under scenarios of a low degree of CO2 emissions promotion with a high degree of CO2 emissions inhibition (LH) and a medium degree of CO2 emissions promotion with a high degree of CO2 emissions inhibition (MH) could achieve the earliest peak time in 2025, with a peak of 434.6 and 439.36 million tonnes, respectively. Discussion: The earliest peak time of CEPI in Shandong Province and Shanxi Province is 2025, but the peak of CEPI in Shanxi is smaller than that of Shandong. The peak time of CEPI in Inner Mongolia is relatively late, in 2030, and the peak is larger than that of the other two provinces. The per capita GDP is the most positive driving factor that contributes to the CEPI. Shandong has a strong economy, and its per capita GDP is much higher than Shanxi's. Therefore, even under the same peak time, the CEPI in Shandong is much higher than that of Shanxi. Inner Mongolia is extensive and sparsely populated, which makes its per capita GDP rank among the top in China. In addition, Inner Mongolia's coal-based power generation structure and high power generation also contribute to its late CO2 peak time and large CO2 peak. [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]

Coal-fired power industry is under enormous pressure to accomplish carbon emission reduction targets. This paper proposes a bi-level multi-objective model for co-firing biomass with coal under carbon cap-and-trade regulation which considers a leader-follower Stackelberg game between the authority and the coal-fired power plants. The upper level regards social welfare maximization and allocation satisfaction maximization as its multiple objectives, while the lower level attempts to maximize the profits of each coal-fired power plant. The inherent uncertainty prompts the motivation for employing fuzzy set theory to characterize the uncertain parameters and determine their exact values. A case study from Shandong Province, China is provided to demonstrate the practicality and efficiency of the optimization model. ε -constraint method and interactive algorithm are used to solve the model, and furthermore the solutions associated with different free carbon emission quota levels and minimal allocation satisfactions have been generated to examine the influences. Based on the analysis and discussion, the methodology can meet the carbon emission reduction goals and transit to a lower-carbon power generation. It also assists the decision makers to develop desired quota allocation strategy in accordance with their attitudes and actual conditions. [ABSTRACT FROM AUTHOR]

Based on the panel data of 17 prefecture-level cities in Shandong Province from 2007 to 2016, this paper studies the decoupling relationship between economic growth and ecological environment pressure in different prefecture-level cities in Shandong Province, and analyses the influencing factors by using decoupling model and LMDI decomposition model. It concludes that the economic growth of the main cities in Shandong Province is relatively decoupled from the pressure of ecological environment; the population and economic factors are the main factors leading to the increase of carbon emissions, and the energy efficiency is constantly improving in China, which contributes to carbon emissions reduction in those areas. On the side, it demonstrates the basis and realistic possibility of the transformation of new and old kinetic energy. [ABSTRACT FROM AUTHOR]

Carbon control in the thermal power generation industry is crucial for achieving the overall carbon peak target. How to predict, evaluate, and balance the allocation of inter provincial carbon emissions has a significant impact on the decision-making of reasonable allocation of inter provincial carbon emissions in the target year. Therefore, this paper uses Monte Carlo-ARIMA-BP neural network and ZSG-DEA model to conduct temporal trend prediction and carbon emission quota allocation research. We propose the "intra provincial and inter provincial" framework for carbon emissions trading in thermal power plants, which aims to break through the barriers in carbon emission rights exchange among provinces. The conclusions are as follows: (1) the growth trend of carbon emissions from thermal power is gradually slowing down and is expected to peak before 2030. (2) Inner Mongolia, Jiangsu, and Shandong have high input–output efficiency, and are all the main output provinces for carbon emission quota allocation. After being adjusted using the ZSG-DEA model, they can still be at the forefront of efficiency. (3) The "intra provincial and inter provincial" framework for carbon emissions trading can effectively predict and allocate the carbon emission demand of each province from time and space dimensions, balance the carbon emission rights and interests of each province, and provide forward-looking planning suggestions for inter provincial carbon emission rights exchange. [ABSTRACT FROM AUTHOR]

Reducing the total carbon emissions of the logistics industry is one of the basic requirements for the development of a low-carbon economy. Based on the AHP analytic hierarchy process and the 1-9 value scaling method, an evaluation index system for the low-carbon development of the logistics industry is constructed. At present, the development of low-carbon logistics requires the combination of low-carbon technology and low-carbon management to ensure the integrity and dynamics of low-carbon logistics control. The results of this study show that in the AHP index analysis of the development of logistics industry in Shandong Province, compared with 13 evaluation indexes of low-carbon logistics and traditional logistics, low-carbon logistics is significantly better than traditional logistics. Among them, the implementation of low-carbon logistics policy system has increased the energy utilization rate to 88.72%, the recovery rate of warehousing and logistics waste to 92.11%, and other relevant indicators have also been improved and optimized to varying degrees. Based on the EKC model curve, the total carbon emissions from 2015 to 2019 showed a downward trend month by month. The future development of low-carbon logistics mainly starts with reducing the total carbon emission of logistics transportation and improving energy utilization. Aiming at the main influencing factors of the development of low-carbon logistics, this paper puts forward specific measures for the development of low-carbon logistics, in order to promote the development of low-carbon logistics. [ABSTRACT FROM AUTHOR]

This paper studies the biomass–coal co-firing (BCC) production planning problem under the carbon emission quota allocation (CEQA) mechanism. However, carbon emission parameters are uncertain due to many factors, such as the type of power units and coal quality. To address this challenge issue, this paper proposes a new globalized robust bi-level optimization model, where the uncertain parameters are characterized by a pair of uncertainty sets. In the proposed model, the upper government as the leader decides the CEQA mechanism, while the lower power plants as the followers develop the production planning according to the given CEQA mechanism. Moreover, based on the Lagrange duality theory and Karush–Kuhn–Tucker (KKT) conditions, the proposed model is equivalently converted into a computationally tractable single-level model. Finally, a practical case study in Shandong Province demonstrates that compared with the nominal bi-level and robust bi-level models, the proposed optimization model can not only effectively resist parameter uncertainty, but also be less conservative. [Display omitted] • A bi-level GRO model for BCC production planning problem under CEQA mechanism is proposed. • The uncertain carbon emission parameters are characterized by a pair of uncertainty sets. • The established globalized robust counterpart is transformed equivalently into a finite convex system. • The effectiveness of the developed GRO approach is verified via a case study. [ABSTRACT FROM AUTHOR]

Combined heat and power is an inevitable trend in the development of waste-to-power industry. Many waste incineration power generation projects that have already put into operation in China are waiting for transition and upgrading to combined heat and power projects. From a growth option perspective, this paper considers the irreversible process and uncertainty in economic benefits, introduces carbon emission trading mechanism and develops the growth option theory to design the expansion decision making model of waste-to-energy combined heat and power projects, so as to obtain the project value and find the optimal expansion timing. In this paper, a case study is carried out on the expansion decision making of a waste-to-energy combined heat and power project in Shandong Province in China. And two scenarios with specific and flexible expansion times are developed from European and American options separately, as European options require investors to expand at a certain time but American options not. The results indicate that 1) real option approach successfully improves the project value through considering uncertainty, and American option can achieve the optimal expansion time, 2) under the carbon emission trading mechanism, the carbon price affects the expansion index, which has a positive effect on the project value, 3) the linear nature of the income and cost expansion index can help investors make judgments on expansion investments. The effectiveness of the case study implies the practicality of the expansion option method for similar projects to reach dual carbon targets in cost and benefit analysis. • Real option approach is applied in energy transition in WTECHP projects. • European and American options are proposed for certain and flexible decision times. • Expansion time and triggers of WTECHP are incorporated into life cycle management. • CCER is introduced as an environmental element to evaluate expansion decision making. [ABSTRACT FROM AUTHOR]

The "double-carbon" policy is a new opportunity for the transformation of China's production sector. With steady economic growth, each province has proposed specific policies aimed at cleaner production. However, the interactions between regions and the complex linkages between industries have hindered the implementation of the "double-carbon" policy. In order to address this issue, we introduced a complex network framework with multiple industries at a national level. The framework aimed to clarify whether there is fluctuation diffusion in China's multi-province multi-industry carbon emission system, to identify key industries and regions, and to answer the question of "who" is the most effective in governance. The results showed that the fluctuations of industrial carbon emissions had a cross-regional diffusion effect in China indeed. The diffusion capacity of industry fluctuation depends on whether the industry is located at a "hub" position in the network. Hub industries with strong capacity can spread the carbon emission fluctuation of themselves and upstream or downstream industries to the whole country through regional interactions. This characteristic of the hub industry should be taken into account in governance to maximize the effectiveness of emission reduction. Shandong and Inner Mongolia, as important provinces for the production of intermediate products and energy chemicals in China, had a greater role to play in global carbon supply push from their hub industries than in the demand pull. The pulling capacity of Beijing-Tianjin-Hebei region to the national carbon demand side was greater than that of Yangtze River Delta and Pearl River Delta. These findings might have implications for environmental and economic policymaking, particularly with regard to cross-provincial coordinated systemic solutions and policy anchors for synergy with industries. [ABSTRACT FROM AUTHOR]

The advancement of the Internet of Things (IoT) technology and its incorporation into the trade economy has altered and improved our way of life and work. However, IoT technologies pose various issues in the green economy, including increased energy consumption, hazardous pollutants, and electronic waste. Therefore, green IoT contributes to a more environmentally friendly atmosphere that is more affordable for the green economy. As a result, it is critical to address tactics and strategies for decreasing pollution threats, traffic waste, resource utilization, energy usage, public safety, living standard, environmental sustainability, and cost management. Apart from these, the continual deepening of the trade economy has accelerated Shandong Province's rapid economic development, and the province's broad economic development mode had major negative consequences on the ecological environment. Based on the evolution of international commerce in Shandong Province and the present status of the ecological environment, this study first calculates the province's carbon emissions using the Kaya identity and derives the regression findings using the Eviews software. It examines the data link between the province's carbon emissions, energy consumption, and trade by determining the province's export carbon emissions from various industrial sectors. According to the statistics, the export carbon emissions of Shandong Province's ferrous metal rolling industry, chemical raw material production sector, and textile industry are relatively high, at 6.764 million tons, 5.502 million tons, and 3.918 million tons, respectively. The carbon emissions from the three industrial sectors mentioned above account for approximately 49% of total carbon emissions, which is a considerable reduction and in line with the concept of green IoT. [ABSTRACT FROM AUTHOR]

Under the dual carbon goals, the relationship between land production-living-ecological function and carbon emissions points to a new direction for land spatial planning. This study compiles and analyzes carbon emissions and the production-living-ecological function coupling coordination degree of 16 cities in the Shandong Peninsula urban agglomeration for the years 2001, 2006, 2011, 2016, and 2021. Furthermore, it introduces the Tapio decoupling model to calculate the decoupling index between carbon emissions and the coupling coordination degree. The spatiotemporal evolution characteristics of production-living-ecological function coupling coordination, carbon emissions, and the decoupling index were analyzed. The results indicate that (1) from 2001 to 2021, the production-living-ecological function coupling coordination degree in Shandong Peninsula urban agglomeration increased overall, with an obvious "high in the east and low in the west" feature in the spatial pattern. That is caused by the difference in resource endowment between the east and the west and the gap in the process of urbanization, industrial structure transformation, and ecological governance. (2) During the study period, overall carbon emissions increased, with a significantly reduced growth rate. A polarization phenomenon of increase and decrease trends within the urban agglomeration was observed. The spatial distribution characteristics of land use carbon emissions showed significant production-living-ecological coupling coordination degree heterotropism. (3) From 2001 to 2021, the decoupling relationship between production-living-ecological coupling coordination degree and carbon emissions mainly exhibited three patterns: strong negative decoupling, expansion negative decoupling, and strong decoupling, maintaining a good decoupling trend overall. These results indicate that the coordinated development level of production, living, and ecological functions in the study area has improved during the research period, and its decoupling relationship with carbon emissions has also shown a positive trend. However, there is still a problem of uneven regional development. In the future, the production-living-ecological development of Shandong Peninsula urban agglomeration should adhere to the development pattern of "two circles and four regions", which aims to promote resource sharing and complementary advantages through specific regional divisions, and achieve coordinated development within the region. This involves optimizing land use structure and function, encouraging innovation and development of green industries, and deepening ecological environment restoration and protection to realize the coordinated development of the production-living-ecological function of land use under the dual carbon goal. [ABSTRACT FROM AUTHOR]

As human consumption expands, four environmental footprints (EFs) exceed the planetary boundaries (PBs) at the global scale. Managing absolute environmental sustainability (AES) based on PBs and EFs at the subnational level is crucial for policy insights. However, a consumption‐based AES management framework still needs to be developed. A framework, including five nexus environmental pressures embodied in the supply chain, was developed and tested in our study across China's 30 provinces to address this knowledge gap. The framework involved three steps: (a) assessing AES for five environmental pressures, (b) measuring environmental surplus and overshoot and composition of EFs, and (c) identifying priority areas for AES management. The results showed that only some provinces are sustainable for three impact‐oriented indicators, especially those with larger populations. Moreover, the embodied environmental pressure mainly flows from the Northwest to Southeast China. For two resource‐oriented indicators, over 74% of provinces are absolutely sustainable. From a nexus perspective, Shandong and Shanghai are identified as priorities for AES management due to their low IESI values of 0.32, 0.33, and 0.40, respectively, which means the worst performance. To improve their IESI, Shanghai needs to control the consumption of blue water‐intensive products, while Shandong and Henan should consume fewer CO2 emissions and N‐ and P‐loss‐intensive products. This framework can clarify subnational responsibilities of environmental overshoots, guide sustainable development, and be widely used at the subnational level in countries worldwide. Plain Language Summary: Earth's ecosystem has a limited ability to provide natural resources and accept pollutants. Absolute environmental sustainability (AES) assumes that irreversible impacts will occur once human needs surpass this capacity, emphasizing the need for policymaking based on the current situation. As a crucial policy‐related entity, subnational regions urge AES management. Here, we focus not only on assessing AES of five environmental pressures (including carbon dioxide (CO2) emission, freshwater use, Nitrogen (N) loss, Phosphorus (P) loss, and land use) embodied in supply chains but also on identifying the priorities of AES management among 30 Chinese provinces. We found that the CO2, N, and P driven by final demands have transgressed the absolute limits in all provinces, especially with larger populations. They mainly consume products from Northwest China. More than 74% of the provinces are within the freshwater and land use boundaries. Shanghai, Shandong, and Henan are identified as priorities of AESA management because of their lousy performance from a nexus perspective. They should be responsible for consumption. Applying this framework globally at the subnational scale is crucial for consumption‐based AES management and global environmental preservation. Key Points: A consumption‐based integrated framework for subnational environmental sustainability management within planetary boundaries was developedAll Chinese provinces overconsumed Nitrogen, Phosphorus, and Carbon dioxide‐intensive productsHenan, Shandong, Shanghai, and Jiangsu were identified as priorities for absolute environmental sustainability management [ABSTRACT FROM AUTHOR]

The cement industry, as one of the primary contributors to global greenhouse gas emissions, accounts for 7% of the world's carbon dioxide emissions. There is an urgent need to establish a rapid method for detecting cement plants to facilitate effective monitoring. In this study, a comprehensive method based on YOLOv5-IEG and the Thermal Signature Detection module using Google Earth optical imagery and SDGSAT-1 thermal infrared imagery was proposed to detect large-scale cement plant information, including geographic location and operational status. The improved algorithm demonstrated an increase of 4.8% in accuracy and a 7.7% improvement in MAP@.5:95. In a specific empirical investigation in China, we successfully detected 781 large-scale cement plants with an accuracy of 90.8%. Specifically, of the 55 cement plants in Shandong Province, we identified 46 as operational and nine as non-operational. The successful application of advanced models and remote sensing technology in efficiently and accurately tracking the operational status of cement plants provides crucial support for environmental protection and sustainable development. [ABSTRACT FROM AUTHOR]

Scientific estimation and dynamic monitoring of CO2 emission trends are an important basis for formulating regional differentiated carbon reduction strategies. Using the integrated nighttime light data, this study estimated CO2 emissions in the Yellow River Basin (YRB) and Yangtze River Basin (YZRB) and discussed the similarities and differences of the spatial distribution of CO2 emissions for the two river basins. The results showed that: (1) The CO2 emissions in the two basins continued to rise, but the growth rate decreased from 2000 to 2018, showing an overall convergence trend, but have not yet reached carbon peak. (2) The high emission and high agglomeration areas were located in Shandong Province in the downstream of the YRB, Shanxi, Shaanxi and Inner Mongolia in the midstream and upstream, and the Yangtze River Delta (YRD). (3) Compared with the YRB, the growth rate of CO2 emissions in the YZRB is slower, and the growth rate declines greatly. In the YRB, it had higher CO2 emissions amount, wider area of high carbon emissions and more obvious spatial agglomeration than that in the YZRB. (4) According to CO2 emissions and economic development level, 220 cities of the two river basins were classified three types: low CO2–low development, high CO2–low development and high CO2–high development. [ABSTRACT FROM AUTHOR]

[Display omitted] • Carbon emissions are explored in China's building operations from provincial perspective. • GDIM is employed to factorize ten affecting factors of CO 2 emissions in building sub-sectors. • CO 2 emissions of building sub-sectors are predicted with dynamic simulation. • The contribution of provinces to the change and peak of national emissions are discussed. Carbon emissions from building operations have a critical impact on achieving carbon peaking goal in China. However, the provincial emission performance in building operations are quite different and it is still unidentified how they affect the national emission change and peaking target, making national emission reduction target hard to implement at the provincial level. This paper explores the driving factors leading to emission changes and examines the prospective emission peaks in China and China's provinces by adopting generalized Divisia index method (GDIM) and the combination of scenario analysis and Monte Carlo simulation. Furthermore, the regional emission heterogeneity and the contribution of provinces to the change and peak of national emissions are discussed to identify regions and provinces that should be focused on emission monitoring. Results show that in China and most Chinese provinces, energy consumption of commercial buildings, energy consumption and floor space of urban residential buildings besides income improvement, and the per capita CO 2 emissions of rural residential buildings contributed greatly to emission growth. Under current policies, commercial and urban residential buildings in most provinces would fail to reach their emission peaks ahead of schedule. The urban residential buildings in the northern region, commercial and rural residential buildings in the eastern region caused higher CO 2 emissions. At the provincial level, Shandong, Shanxi and Hebei made the top contribution to total emissions of commercial, urban residential and rural residential buildings, respectively. Shandong and the provinces in north China of urban residential buildings and four provinces including Shandong, Guangdong, Hebei and Zhejiang of commercial and rural residential buildings had the significant impact on the uncertainty of the peaking time of CO 2 emissions. These findings provide a strong decision-making reference for Chinese government to formulate targeted regional emission reduction policies in building operations. [ABSTRACT FROM AUTHOR]

• The real decoupling of economic growth and carbon emissions is the basis for China to realize the sustainable development of low-carbon economy. To maintain the sustainable development trend of China's economy, it is necessary to get rid of the ties between fossil energy and control and reduce carbon emissions. From a local perspective, this paper selects a specific province in China and empirically analyzes the correlation between carbon emissions, energy consumption and economic growth in 1994–2016. • The ultimate goal of sustainable and low-carbon economic transformation and development is to realize the complete decoupling of economic growth and carbon emissions. Therefore, it is important to understand the development and change of decoupling relationship between variables. Based on EKC hypothesis and Tapio decoupling theory, this paper empirically analyzes the decoupling relationship in the time interval and at the time node, and finds out the actual situation of decoupling relationship between energy environment and economic growth in Shandong Province. • According to the extension method of KAYA identity, the calculation formula of carbon emissions is transformed, and a carbon emission prediction model including constraints is constructed. According to the decoupling relationship theory, different decoupling situations of carbon emission and economic growth are set up to judge the future decoupling status. In this way, "decoupling" and "prediction" are effectively combined. Then, combined with scenario analysis method, the total carbon emission and its annual growth rate in 2016–2030 are predicted, and the decoupling status between carbon emission and economic growth is predicted. Based on the change of carbon emission in the future, the possible peak year of carbon emission under different scenarios is analyzed. Under the low-carbon environment of economy and society, the real decoupling between economic growth and high carbon emissions is the basis for realizing the sustainable and low-carbon transformation and development of Shandong Province. This paper uses energy consumption as an intermediate variable, according to EKC hypothesis theory and Tapio decoupling indicator theory, by constructing the multiple regression model and low-carbon decoupling model of carbon emissions, energy consumption and economic growth, this makes an empirical analysis of the interval elasticity relationship and the point-in-time elasticity relationship in 1994−2017. The decoupling situation is: there is an inverted "N" curve relationship between carbon emissions and economic growth in Shandong Province. The second theoretical turning point is in 2012, and the decoupling relationship will gradually appear in the future. To a great extent, Shandong Province's low-carbon decoupling indicator is affected by the energy conservation decoupling indicator, and the strong decoupling state of economic growth and carbon emissions has appeared continuously, but it is not stable enough. The ideal decoupling state between economic growth and carbon emission will occur only after carbon emission reaches the peak, so it is necessary to predict the peak year of carbon emission. Based on the scenario analysis and decoupling theory, a carbon emission model is constructed to analyze the carbon emission status and its possible peak years in different scenarios in Shandong Province from 2016 to 2030. On the whole, with the improvement of economic development level, the situation of low energy consumption and low emissions is gradually significant. The degree of decoupling is mainly affected by the macro-economic situation and the change of policy regulation. Therefore, the paper puts forward the scientific, feasible and effective policy measures with Shandong characteristics to promote the sustainable development of low-carbon economy. [ABSTRACT FROM AUTHOR]

Promoting the low-carbon development of the power sector is an important means for China to achieve the emissions peak and carbon neutralization targets, while the rational allocation of emission reduction responsibility is the prerequisite for low-carbon electricity. This paper proposes a two-level mechanism to allocate CO 2 emission reduction responsibilities based on the principles of fairness, efficiency and sustainability. First, a graph game model considering the distribution of regional resources and mutual cooperation is established to initially allocate national emission reduction responsibilities to 8 regions in 2019. Then, taking the inter-provincial transfer of CO 2 emissions into consideration, the Game-theory combination weighting method is used to allocate the regional responsibilities to corresponding provinces. The results show that the Southwest has the least responsibility (−0.24%), while the North and East are responsible for +28.66% and +26.64%, respectively. At provincial level, Inner Mongolia (+10.13%), Shandong (+8.56%), Jiangsu (+8.16%) and Hebei (+7.12%) have a greater responsibility for emission reduction, while the provinces with the least responsibility are Chongqing (−0.06%) and Sichuan (−0.18%). To further explore how the responsibility will change in the future and what can be improved for each province, three scenarios are set up to allocate the responsibility in 2025, 2035 and 2050, and highlight the changes during the 14th Five-Year Plan period. If the growth rate of power demand increases, the responsibility of Northwest (+0.89%) will increase most compared to BAU, among which Xinjiang (+0.26%) and Ningxia (+0.23%) with low efficiency and unclean power generation will significantly bear more responsibilities. If the non-fossil growth rate increases, the large-scale electricity consumers such as East (+0.27%) and North (+0.25%) will show the largest change, with Jiangsu and Shandong account for +0.09% and +0.08%, respectively. • Consider the actual transmission situation of China's power grid and the transfer of embodied CO 2 emission. • Propose a two-level mechanism to allocate CO 2 emission reduction responsibilities. • Set up three scenarios to explore how responsibility will change in the future and what can be improved for provinces. [ABSTRACT FROM AUTHOR]

The integrated energy system which coordinates natural gas, renewable energy, and other energy subsystems is an effective way to promote a low-carbon economy. An effective framework for system assessment and optimisation is a critical issue. This paper takes a natural gas-wind-photovoltaic integrated energy system as the research object and uses the simulation software to analyse its techno-enviro-economic feasibility. Firstly, a mathematical model is customised to optimise the system installation and operation plans. Renewable electricity replaces some natural gas, resulting in pipeline pressure fluctuation. Here, the Stoner Pipeline Simulator software is used to simulate pipeline network operation to quantify the aforementioned pressure fluctuations. The proportion of renewable energy is gradually reduced until the network pressure fluctuation is less than 20% to ensure the stability of pipeline operation. Then, the optimal operation scheme can be determined. Taking three cities in Shandong, China, as cases, the results show that the proposed system is beneficial for urban energy internet development: (i) the total net present cost is reduced by 19.7%, 19.8%, and 20.8%, (ii) annual CO 2 emission is reduced by 23.7%, 18.4%, and 12.2%; (iii) the levelised cost of energy is 0.142 $/kWh, 0.143$/kWh, and 0.153$/kWh. [ABSTRACT FROM AUTHOR]

A series of carbon dioxide (CO2) emission inventories with high spatial resolutions covering China have been developed in the last decade, making it possible to assess not only the anthropogenic emissions of large administrational units (countries; provinces) but also those of small administrational units (cities; counties). In this study, we investigate three open-source gridded CO2 emission inventories (EDGAR; MEIC; PKU-CO2) and two statistical data-based inventories (CHRED; CEADs) covering the period of 2000–2020 for 16 prefecture-level cities in Shandong province in order to quantify the cross-inventory uncertainty and to discuss potential reasons for it. Despite ±20% differences in aggregated provincial emissions, all inventories agree that the emissions from Shandong increased by ~10% per year before 2012 and that the increasing trend slowed down after 2012, with a quasi-stationary industrial emission proportion being observed during 2008–2014. The cross-inventory discrepancies increased remarkably when downscaled to the city level. The relative differences between two individual inventories for half of the cities exceeded 100%. Despite close estimations of aggregated provincial emissions, the MEIC provides relatively high estimates for cities with complex and dynamic industrial systems, while the CHRED tends to provide high estimates for heavily industrial cities. The CHRED and MEIC show reasonable agreement regarding the evolution of city-level emissions and the city-level industrial emission ratios over 2005–2020. The PKU-CO2 and EDGAR failed to capture the emissions and their structural changes at the city level, which is related to their point-source database stopping updates after 2012. Our results suggest that cross-inventory differences for city-level emissions exist not only in their aggregated emissions but also in their changes over time. [ABSTRACT FROM AUTHOR]

Since the combustion of fossil fuel became the largest contributing factor of greenhouse gas emissions in China, the growth of energy consumption caused by urbanization has led to significant increase in carbon emissions. Applying both the carbon-emissions-coefficient and the sector-energy-consumption method, we predicted the energy consumption and carbon emissions, explored the characteristics of energy consumption and carbon emissions in different urbanization stages, and analyzed the energy crisis in Shandong which is a typical province in China. The results indicate that in 2015 and 2020, the total primary energy consumption will be 1.57 times and 1.85 times higher than that of in 2009, and the carbon emissions are estimated to be 1.48 times and 1.67 times respectively. The urbanization has distinct positive effects on energy consumption and carbon emissions, and the influence is more obvious in the intermediate-stage than in the early-stage. From a policy perspective, this paper could provide valuable information for planners and decision makers to formulate feasible and practical polices with a view to optimize energy structure and mitigate carbon emissions. [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]

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