山东省能源消费碳排放时空格局研究

Causal Relationships between Energy Consumption and Economic Growth

Shandong Province is a province with strong economic growth and high energy consumption in China. The energy consumption structure is mainly composed of coal and oil. While the regional GDP is increasing with each passing year, carbon emissions are also increasing year by year, and carbon emissions are mainly concentrated in high energy consuming industries. Coal plays a very significant part in the growth of social economy and has become a necessity for people's production and life. The coal based chemical industry is one of the six leading industries of Shandong Energy Group. The coal chemical industry is a significant component of China's energy construction, and how to improve the quality and efficient utilization of coal chemical products is currently the focus of close attention in the coal chemical industry. Promoting economic transformation and upgrading, as well as energy structure adjustment, is a necessary path for China to achieve the objective of "carbon peaking and carbon neutrality". Carbon peaking and carbon neutrality "is the main foundation and new engine for China's economic and social growth in the next 40 years, which will inevitably drive systematic changes such as low-carbon transformation, structural reshaping, and technological innovation in the energy industry. This article takes Shandong Province as an example to study the current situation of China's energy economy structure and economic development trends.

China's cement industry, which produced 1,388 million metric tons (Mt) of cement in 2008, accounts for almost half of the world's total cement production. Nearly 40% of China's cement production is from relatively obsolete vertical shaft kiln (VSK) cement plants, with the remainder from more modern rotary kiln cement plants, including plants equipped with new suspension pre-heater and pre-calciner (NSP) kilns. Shandong Province is the largest cement-producing Province in China, producing 10% of China's total cement output in 2008. This report documents an analysis of the potential to improve the energy efficiency of NSP kiln cement plants in Shandong Province. Sixteen NSP kiln cement plants were surveyed regarding their cement production, energy consumption, and current adoption of 34 energy-efficient technologies and measures. Plant energy use was compared to both domestic (Chinese) and international best practice using the Benchmarking and Energy Saving Tool for Cement (BEST-Cement). This benchmarking exercise indicated an average technical potential primary energy savings of 12% would be possible if the surveyed plants operated at domestic best practice levels in terms of energy use per ton of cement produced. Average technical potential primary energy savings of 23% would be realized if the plants operated at international best practice levels. Energy conservation supply curves for both fuel and electricity savings were then constructed for the 16 surveyed plants. Using the bottom-up electricity conservation supply curve model, the cost-effective electricity efficiency potential for the studied cement plants in 2008 is estimated to be 373 gigawatt hours (GWh), which accounts for 16% of total electricity use in the 16 surveyed cement plants in 2008. Total technical electricity-saving potential is 915 GWh, which accounts for 40% of total electricity use in the studied plants in 2008. The fuel conservation supply curve model shows the total technical fuel efficiency potential equal to 7,949 terajoules (TJ), accounting for 8% of total fuel used in the studied cement plants in 2008. All the fuel efficiency potential is shown to be cost effective. Carbon dioxide (CO{sub 2}) emission reduction potential associated with cost-effective electricity saving is 383 kiloton (kt) CO{sub 2}, while total technical potential for CO{sub 2} emission reduction from electricity-saving is 940 ktCO{sub 2}. The CO{sub 2} emission reduction potentials associated with fuel-saving potentials is 950 ktCO{sub 2}.

Hemorrhagic fever with renal syndrome (HFRS) is a widespread zoonotic disease transmitted by rodents, posing a serious public health threat in People's Republic of China. Due to the higher incidence of HFRS occurred in Shandong Province, this study aims to understand the spatiotemporal pattern of HFRS, identify the driving factors and predict potential high-risk areas in Shandong Province, to provide guidance for public health policy making. Case information on HFRS occurred in Shandong Province from 2018 to 2024 was collected from the China Information System for Disease Control and Prevention (CISDCP). Incidence rate of HFRS was calculated monthly and annually to explore its preliminary distribution trend. Spatiotemporal scanning analysis was used to determine the temporal and spatial clustering characteristics of HFRS cases. The maximum entropy (MaxEnt) model was employed to explore the major factors influencing HFRS and predict high-risk areas of HFRS in Shandong Province. From 2018 to 2024, a total of 4,837 cases of HFRS were reported in Shandong Province, with the incidence rate showing a fluctuating downward trend. The peak incidence period occurred annually from October to December. Spatiotemporal scanning analysis showed the first cluster involved 29 counties across 5 prefecture-level cities in eastern Shandong Province, spanning October to November 2018. The second cluster involved 16 counties across 6 prefecture-level cities in central Shandong Province, spanning November to December 2021. The third cluster area involved 4 counties across 2 prefecture-level cities in southwestern Shandong Province, spanning March to April 2018. The fourth cluster area was located in Shanghe County, north of Jinan City, spanning November to December 2021. When optimizing the MaxEnt model, the optimal performance was achieved with the feature class (FC) set to linear, quadratic, hinge, product, and threshold (LQHPT) and the regularization multiplier (RM) set to 0.2. Yearly average air temperature, normalized difference vegetation index, yearly average relative humidity and yearly average sunshine duration were identified as the main factors influencing the occurrence of HFRS. The risk prediction map showed that high-risk areas for HFRS were primarily concentrated in the eastern and central regions of Shandong Province, covering an area of 26682.92 square kilometers, accounting for 16.90% of the province's total area. HFRS in Shandong Province exhibited obvious spatiotemporal patterns and was influenced by multiple factors, including temperature, vegetation, humidity and sunshine. These findings highlight the need for health authorities to integrate environmental and socio-economic considerations into the design of strategy or countermeasures against HFRS, particularly during high-incidence season and in high-risk areas.

With peoples growing awareness of green economy, low-carbon economic development has been increasingly demanded. The relationship of energy consumption structure and economic growth can be an important indicator for an economic entitys ecological economy process. With the implementation of the Shandong peninsula blue economic zone in 2011, Shandong Province has been developing rapidly in recent years. Taking Shandong economic entity as the research object, the paper analyses the correlation between the economic development and different kinds of energy consumption, including coal, oil, electricity and new energy, with the grey correlation model using the data from 2000 to 2009 that representing the economic development and energy consumption. The result of the study shows that new energy consumption has the great correlation with economic development indicating that Shandong emphasizes on the development and utilization of new energy to reduce the excessive consumption of non-renewable energy in its economic growth.

The scientific prediction of energy consumption plays an essential role in grasping trends in energy consumption and optimizing energy structures. Energy consumption will be affected by many factors. In this paper, in order to improve the accuracy of the prediction model, the grey correlation analysis method is used to analyze the relevant factors. First, the factor with the largest correlation degree is selected, and then a new grey multivariable convolution prediction model with dual orders is established. Different fractional orders are used to accumulate the target data sequence and the influencing-factor data sequence, and the model is optimized by particle swarm optimization algorithm. The model is used to fit and test the energy consumption of Shanghai, Guizhou and Shandong provinces in China from 2011 to 2020 compared with other multivariable grey prediction models. Experimental results with the MAPE and RMSPE measurements show that our improved model is reasonable and effective in energy consumption prediction. At the same time, the model is applied to forecast the energy consumption of the three regions from 2021 to 2025, providing reliable information for future energy distribution.

Analyzing the driving factors and mechanisms of urban carbon emission correlation networks can provide effective carbon reduction decision-making support for Shandong Province and other regions with similar industrial characteristics. Based on industrial carbon emission data from various cities in Shandong Province from 2013 to 2021, the spatial correlation network of carbon emission was established by using a modified gravity model. The characteristics of the network were explored by using the Social Network Analysis (SNA) method, and significant factors affecting the network were identified through Quadratic Assignment Procedure (QAP) correlation analysis and motif analysis. The driving mechanism of the carbon emission correlation network was analyzed by using Temporal Exponential Random Graph Models (TERGMs). The results show that: (1) The spatial correlation network of urban carbon emission in Shandong Province exhibits multi-threaded complex network correlations with a relatively stable structure, overcoming geographical distance limitations. (2) Qingdao, Jinan, and Rizhao have high degree centrality, betweenness centrality, and closeness centrality in the network, with Qingdao and Jinan being relatively central. (3) Shandong Province can be spatially clustered into four regions, each with distinct roles, displaying a certain “neighboring clustering” phenomenon. (4) Endogenous network structures such as Mutual, Ctriple, and Gwesp significantly impact the formation and evolution of the network, while Twopath does not show the expected impact; FDI can promote the generation of carbon emission reception relationships in the spatial correlation network; IR can promote the generation of carbon emission spillover relationships in the spatial correlation network; GS, differences in GDP, differences in EI, and similarities of IR can promote the generation of organic correlations within the network; on the temporal level, the spatial correlation network of urban carbon emission in Shandong Province has shown significant stability during the study period.

Based on the data of 859 scenic villages in Shandong Province, the spatial pattern of scenic villages in Shandong Province and various prefectural cities and the factors affecting them were quantitatively studied by using spatial analysis methods such as average nearest neighbor index method and kernel density analysis, as well as combining with geodetic detector analysis tools. The results show that: (1) the scenic villages in Shandong Province have significant aggregation distribution characteristics, but there are differences in the spatial types of scenic villages in different prefectural-level cities; (2) the scenic villages in Shandong Province show the spatial distribution characteristics of "three core dense areas and multiple sub-core dense areas", and the spatial distribution in different regions has significant differences. The spatial distribution of scenic villages in Shandong Province is characterized by The "three core intensive areas" are Taian-Jinan, Zibo-Weifang central Luzhong core agglomeration area and Rizhao-Qingdao east Luzhong core intensive area; (3) scenic villages in Shandong Province are affected by a combination of natural conditions, socio-economic conditions, tourism resource conditions, and transportation conditions, among which there is a strong dependence on river density and the number of tourists received.

This paper analyzes the energy consumption of Shandong Province by the use of Complete Decomposition Model. We decompose three key factors: economic growth, structure and energy intensity, and study the impact on energy consumption results brought by changes in these variables. The corresponding effect coefficients is compared with national average level to identify the characteristics of energy consumption changes in Shandong and specific effects of every stage of each factor, providing a scientific basis for the development of energy policy making.

Properties of particulate matter and gaseous pollutants in Shandong, China: Daily fluctuation, influencing factors, and spatiotemporal distribution

With the motivation to explore China’s nationally determined contributions (NDCs) toward a net zero emission future, we examine whether green technology innovation mitigates carbon dioxide (CO 2 ) emission thereby improving environmental quality across five of the most populous provinces (consisting of 85 cities) for the period between 2007 and 2019 in a multidimensional panel approach with an endogeneity robustness from two-step system generalized method of moments (GMM). The results show green technology innovation, economic output (GDP), and financial development spur CO 2 emission in the across the provinces. Meanwhile, especially in the whole panel, green technology innovation is dependent on the level of industrial structure (a moderation effect), but this interaction effect fails to show desirable outcome in the province-specific cases. Additionally, in each of Guangdong, Henan, Hunan, Shandong, and Sichuan provinces, carbon emission is triggered by an increase in GDP and financial development. Additionally, green technology innovation (i) worsens carbon emission through the moderating effect of advanced industrial structure and industrial structure rationalization in Guangdong and Sichuan provinces (ii) worsens carbon emission through the moderating effect of rationalization of industrial structure in Henan, Sichuan, and Shandong provinces. These findings have vital policy insight toward improving the quality of green innovations in China. Graphical Abstract

The increasing population aging in China has led to a growing demand for nursing homes. The indoor thermal comfort of nursing homes affects the occupants’ quality of life, building energy consumption, and carbon emissions. This study used thermal comfort questionnaires, environment tests, and physiological parameter tests to conduct a field survey of 954 occupants (including the elderly and the adult staff) in nursing homes in Shandong Province, China, and analyzed the thermal comfort of occupants. Results showed that in Shandong Province, there was a significant difference in thermal sensation between the elderly and adults under the same conditions. The neutral temperatures for the elderly and adults were 21.7 and 20.5 °C, the comfort temperature ranges were 19.4–24.0 °C and 18.6–22.5 °C, and the preferred temperatures were 23.8 and 23.1 °C, respectively. The elderly prefer higher temperatures than adults. Personal clothing insulation was significantly negatively correlated with operative temperature. Occupants’ average skin temperature was significantly positively correlated with operative temperature and mean thermal sensation votes. Based on the simulation results of building energy consumption and carbon emissions, this paper proposes design strategies for nursing homes that balance thermal comfort and energy savings.

Industrial structure and regional innovation have a significant impact on emissions. This study explores, from the multivariate coupling and spatial perspectives, the degree of coupling coordination between three factors: industrial structure, carbon emissions, and regional innovation of 97 counties in Shandong Province, China from 2000 to 2017. On the basis of global spatial autocorrelation and cold and hot spots, this article analyzes the spatial characteristics and aggregation effects of coupled and coordinated development within each region. The results are as follows. (1) The coupling degree between carbon emissions, industrial structure, and regional innovation in these counties fluctuated upward from 2000 to 2017. Coupling coordination progressed from low coordination to basic coordination. Regional differences in coupling coordination degree are evident, showing a stepped spatial distribution pattern with high levels in the east and low levels in the west. (2) During the study period, the coupling coordination showed a positive correlation in spatial distribution. Moran’s I varies from 0.057 to 0.305 on a global basis. Spatial clustering is characterized by agglomeration of cold spots and hot spots. (3) The coupling coordination exhibited significant spatial differentiation. The hot spots were distributed in the eastern part, while the cold spots were located in the western part. The results of this study suggest that the counties in Shandong Province should promote industrial structure upgrades and enhance regional innovation to reduce carbon emissions.

Alteration Information Extraction by Applying Synthesis Processing Techniques to Landsat ETM+ Data: Case Study of Zhaoyuan Gold Mines, Shandong Province, China

China's economy has transtioned into the “new normal”, which demands higher standards for energy utilization efficiency. Meanwhile, the spatial distribution of China's energy consumption and economic development exhibits a significant imbalance, complicating efforts to achieve Pareto optimization of regional energy allocation efficiency. Addressing this issue, this study explores the heterogeneity of the factors influencing energy consumption in China from the dual perspectives of economic “new normal” and geographic space, using an exponential decomposition model. The results of the study show that: (1) the inhibitory effect of the energy intensity effect on the growth of regional energy consumption is differentiated, with stronger inhibitory effects in Guangdong, Jiangsu and other provinces, and weaker inhibitory effects in Hainan, Qinghai, and other provinces. Living standard effect on the regional energy consumption growth of the promotion of the effect also has differences, Jiangsu, Shandong and other provinces of the promotion of the effect is stronger, while Hainan, Qinghai, and other provinces of the promotion of the effect is weaker. (2) Population size effect on regional energy consumption growth is not consistent in the direction of the role of Guangdong, Zhejiang, and other provinces have a promotional effect and the role of the effect of the stronger, on the contrary, the provinces of Heilongjiang, Jilin, and Gansu has an inhibitory effect. (3) In the late stage of the economic “new normal”, the effects of energy intensity effect, living standard effect and population size effect on the growth of energy consumption in the four regions show a weakening trend, and this weakening trend is more obvious in the northeast region. This study expands theoretical research on factors affecting energy consumption and offers practical guidance for China's government to coordinate regional energy allocation under the economic “new normal” and geographical considerations.