Proportion Of Primary Industry Research Articles

Spatiotemporal Patterns and Driving Factors of Non-Grain Cultivated Land in China’s Three Main Functional Grain Areas

Food security, fundamental to national security, is challenged by the non-grain conversion of cultivated land. Based on the social and economic statistical data in China, this paper explores the spatiotemporal patterns and driving factors of non-grain cultivated land nationwide and in China’s three main functional grain areas during 2000–2020 with the help of the GIS Spatial Analysis and Spatial Metrology Model. The results show, first, that non-grain conversion initially increased but later decreased, with the non-grain level increasing from 30.61% in 2000 to 34.78% in 2003 and then decreasing to 30.28% in 2020; vegetables, fruits, and medicinal herbs were the main non-grain crops in most areas. Second, the non-grain levels showed an obvious spatial agglomeration state; the regions with low non-grain levels were located in the main grain-producing areas, and although the non-grain conversion levels in the main grain-producing areas decreased, the non-grain levels in these areas were clearly lower in the north than in the south. Moreover, the non-grain conversion levels in the main grain-sales areas and the grain production and sales balance areas increased. Third, rural population size, per capita GDP, the proportion of primary industry, and the land transfer rate are important drivers of the non-grain conversion of cultivated land nationally, but there are also significant spatial differences in the influence of these driving factors in different functional grain areas. This paper provides a new research perspective for analyzing the influencing mechanisms of cultivated non-grain land from the three functional grain areas. At the same time, it reveals the roots of the government’s predicament in the governance of non-grain cultivated land. It provides a reference for the government to formulate new policies for managing non-grain cultivated land.

Spatial-temporal evolution and driving factors of ecological security in China based on DPSIR-DEA model: A case study of the Three Gorges reservoir area

Ecological security (ES) is an important element in the construction of an ecological civilization and plays a vital role in ensuring the sustainable development of social economies. Further, ES is an important measure of regional sustainable development. Based on ecosystem theory, this study constructs a comprehensive evaluation framework using the driving force-pressure-state-impact-response (DPSIR) and slacks-based measure of data envelopment analysis (SBM-DEA) models. Taking the Three Gorges Reservoir Area as an example, the study develops an ES evaluation system and realizes a transformation from “efficiency” to “quality”.Gray correlation theory and spatial autocorrelation and are adopted to analyze the ecosystem's spatial–temporal evolution and driving factors. The results demonstrate that the overall level of ES in the Three Gorges Reservoir Area from 2010 to 2020 was good; however, the spatial distribution of ES revealed an unbalanced state. The influence of each subsystem on the result of the ES evaluation is state > response > impact > driving force > pressure. The spatial correlation analysis of ES in the Three Gorges Reservoir area exhibited a positive autocorrelation, and the agglomeration effect was significant; on this basis, the key factors affecting ES were identified. The ratio of environmental protection investment, sewage treatment rate, green coverage rate of built-up areas, proportion of primary industry in GDP, and proportion of tertiary industry in GDP had notable effects on the ES of the study area. The research contributes to the theory of ES and provides guidance for ecologically sustainable development.

The change of ecosystem resilience and its response to economic factors in Yulin, China

Ecosystem resilience at a regional scale is an important indicator and component of ecological health assessment. Ecosystem resilience refers to the self-regulating and restoring capacity of an ecosystem to restore itself to its initial state after deviation from the equilibrium state due to disturbances. In this study, Fragstats and ArcGIS software were used to calculate various indicators of ecosystem resilience, then a model of ecosystem resilience was used to evaluate and analyze the characteristics of ecosystem resilience in 12 counties of Yulin Prefecture, China. The ecosystem resilience–economy responses were discussed using Pearson correlation coefficients and multiple regression analysis accordingly. The results suggested that ecosystem resilience in Yulin increased steadily during 1995–2015, with the self-regulating and self-restoring capacity strengthened over time. The study also found that the total output value of agriculture, forestry, animal husbandry, and fishery, fiscal expenditure and gross investment in fixed assets were significantly and positively correlated with ecosystem resilience, with correlation coefficients of 0.716 (p = 0.000 < 0.01), 0.589 (p = 0.002 < 0.01) and 0.411 (p = 0.046 < 0.05), respectively. The proportion of primary industry were negatively correlated with ecosystem resilience, with correlation coefficients of −0.445 (p = 0.029 < 0.05). The research revealed the development and changes of ecological resilience in Yulin and the response to the social economy. The findings provided support for ecological health diagnosis and decision-making concerning sustainable development in the study area and beyond.

Industrial Poverty Alleviation, Digital Innovation and Regional Economically Sustainable Growth: Empirical Evidence Based on Local State-Owned Enterprises in China

This paper takes the industrial poverty alleviation of local state-owned enterprises in China as the research object, and takes the local state-owned enterprises listed in Shanghai and Shenzhen A shares in China from 2016 to 2020 as the sample to empirically test the impact of industrial poverty alleviation on the sustainable economic growth of the region and consider the regulatory effect of digital innovation. This study found that China’s local industrial poverty reduction behavior in state-owned enterprises can effectively promote regional economic growth. Moreover, the digital innovation produced a regulating effect; that is, if enterprises carry out digital innovation and have a higher degree of digital innovation, their industrial poverty alleviation behavior will have a stronger role in promoting regional economic growth. This conclusion still holds even after controlling for factors of robustness and endogeneity. In addition, the study of influence mechanisms found that the proportion of primary industry in GDP was the mediating effect of industrial poverty alleviation on regional economic growth, and the proportion of primary industry in GDP had a partial mediating effect. Further heterogeneous group testing shows that the impact of industrial poverty alleviation on regional economically sustainable growth is more obvious in agriculture-related, local state-owned enterprises; non-high-tech, local state-owned enterprises; and local state-owned enterprises with subsequent poverty alleviation plans. The empirical evidence in this paper verifies the role of local state-owned enterprises’ participation in industrial poverty alleviation in promoting regional economic growth. It is a useful supplement to the research literature on the economic consequences of Chinese enterprises’ participation in targeted poverty alleviation, which helps to better understand such a phenomenon and also provides a powerful explanation for China’s poverty alleviation achievements.

Spatiotemporal Evolution and Influencing Factors of the Rural Natural Capital Utilization Efficiency: A Case Study of Chongqing, China

The efficient utilization and optimal allocation of natural capital play an important role in economic development and human well-being. The production process of natural capital is the input and output processing of its ecological resources and the environment. Improving the rural natural capital utilization efficiency (RNCUE) is an important goal of natural capital investment, and the efficient utilization of natural capital is an important factor for the efficient operation of the regional economy and society. This study uses the super slack-based measure (SBM) model based on undesirable outputs to measure the RNCUE, combines the exploratory data analysis method (ESDA) and spatial Markov transfer matrix to analyse the spatiotemporal evolution characteristics of efficiency, and analyses the influencing factors of the change of the RNCUE in Chongqing through a spatial econometric model. The results show that: (1) The RNCUE in Chongqing is still at a low level as a whole and there is a large space for efficiency improvement and efficient operation. There is a certain spatial dependence on the interaction of efficiency between adjacent districts and counties. (2) High-high agglomeration is concentrated in the western area of Chongqing One-hour Economic Circle, and low-low agglomeration is concentrated in Southeast and Northeast Chongqing. The probability of a large change in the RNCUE in consecutive years is small, and it is easy to form the phenomenon of “club convergence” in space. (3) The RNCUE in Chongqing has been affected by rainfall, temperature, NDVI, the per capita GDP, proportion of fixed asset investment, expenditure for agriculture, and proportion of primary industry and rural population. The influencing factors show that the spatial heterogeneity is significant. The RNCUE has a negative correlation with forest coverage and the expenditure for agriculture, is not significantly positive or negative with the proportion of the primary industry and is positively correlated with the rural population density. This study points out that we can improve the RNCUE in Chongqing by optimizing the spatial differentiation control mechanism, clarifying property rights, enhancing liquidity, and strengthening scientific and technological innovation.

Spatiotemporal variation of nitrogen and phosphorus and its main influencing factors in Huangshui River basin

The foundation of managing excess nutrients in river is the identification of key physical processes and the control of decisive influencing factors. The existing studies seldom consider the influence of rainfall-runoff relationship and only focus on a few anthropogenic activities and natural attributes factors. To address this issue, a comprehensive set of influencing factors including rainfall-runoff relationship (represented by runoff coefficient), basic physical and chemical parameters of water quality, land use types, landscape patterns, topography, and socioeconomic development was constructed in this study. M–K test and cluster analysis were conducted to identify the temporal mutation and spatial clustering characteristics of NH3-N and TP in Huangshui River basin, respectively. Partial least squares regression was used to elucidate the linkages between water contaminants and the factors. As shown in the results, the temporal mutations of NH3-N and TP were obvious in the middle reaches, with 4 out of 7 catchments in the middle reaches have a larger number of mutations of NH3-N than other catchments. The cluster analysis results of NH3-N and TP among catchments were similar. This study also indicated that although the Huangshui River basin was located in the upper reaches of the Yellow River, the influences of rainfall-runoff relationship on spatiotemporal changes of NH3-N and TP in its sub-basins were limited. Only the temporal change of NH3-N in Jintan catchment in the upstream area was significantly affected by runoff coefficient. The indexes of proportion of water area (PWA), proportion of impervious area (PIA), and proportion of primary industry (PPI) were the top three influencing factors of temporal variation of NH3-N and TP for most catchments in the middle reaches. The temporal change of NH3-N in Jintan catchment in the upstream area was obviously affected by runoff coefficient. The spatial variation of NH3-N and TP were all affected by PWA and proportion of secondary industry significantly. The results of this study can provide theoretical basis and technical support for the control and management of nitrogen and phosphorus pollution in upper reaches of rivers.

Mapping ecological space quality changes for ecological management: A case study in the Pearl River Delta urban agglomeration, China

Compiling information concerning changes in ecological space quality (ESQ) is imperative for urban management and restoration, as proper management promotes sustainable development. Most previous studies have lacked a comprehensive model for evaluating ESQ and are thus unable to provide effective support for decision-makers. Based on the purpose of policy and needs of the public, this paper constructs a comprehensive adaptive evaluation model for mapping ESQ using the Pearl River Delta (PRD) urban agglomeration as an example, and the analysis uncovers the driving forces of urbanization indicators of ESQ change. From 2000 to 2017, the overall ESQ was considered as good, but the overall value decreased slightly, from 52.8 to 51.5. ESQ in the central PRD exhibited a notable downward trend, while coastal cities exhibited an upward trend. There was an approximate negative correlation between ESQ and the urbanization indexes, except for education level and the proportion of primary industry. In the PRD, rural population density, the proportion of primary industry, and education level were the important drivers of magnitude and direction in most cities, but their impacts differed across cities. The ecological management lacked control of in areas good and moderate ESQ, and this was the main factor resulting in the decline of regional ESQ. By quantifying ESQ and the spatially explicit urbanization drivers, the potential for ecological management in the urban agglomeration is also discussed.

Spatiotemporal variation analysis of regional flood disaster resilience capability using an improved projection pursuit model based on the wind-driven optimization algorithm

Abstract Due to the weak methods available for evaluation of the resilience of regional flood disaster systems and the lack of research on the driving mechanism of resilience, by exploring the principles of regional flood disaster resilience and constructing a suitable evaluation index system, the wind driven optimization (WDO) algorithm was introduced, and an improved projection pursuit (PP) evaluation model of flood disaster resilience was proposed. Twelve farms under the Heilongjiang Agricultural Reclamation Hongxinglong Administration Bureau were included in the research area. A total of 43 primary indicators were selected from four criteria to describe the natural environment, culture, society, economic development and flood control technologies. The R clustering factor analysis method was used to determine 15 optimal indexes. The improved PP model based on the WDO algorithm (WDO-PP) was used to evaluate the flood disaster resilience of 12 farms. The results showed that the number of farms with a level IV rating on flood resilience decreased from 25% to 8.3% from 2002 to 2009. In 2009–2016, with the exception of the Bawuer and Shuguang farms, the flood disaster resilience index decreased, and that of the remaining farms increased. In 2002–2016, the Wujiuqi, Shuangyashan, Shuguang and Hongqiling farms in the central region of the Hongxinglong Administration Bureau were less resilient to disasters, and the farms that responded better to flood disasters were mainly located in the eastern or western Hongxinglong Administration Bureau near a river. Further analysis shows that the forest coverage rate, paddy field coverage ratio, shelter forest area ratio, proportion of primary industry, agricultural water use efficiency, and irrigation and drainage capacity were the key drivers of the flood disaster resilience in the Hongxinglong Management Bureau. Based on the Rastrigin and Schaffer functions, the results show that the success rate of the WDO algorithm is 100% over 10 iterations of the optimization calculation of the test function, while the success rate of the other two algorithms is relatively inadequate; however, in terms of value and standard deviation, both are better than adaptive particle swarm optimization (APSO) and adaptive genetic algorithm (AGA) algorithms. Moreover, in the convergence curve, the WDO algorithm converges fast, the number of iterations can achieve the optimal effect on average 3–5 times, and the AGA and APSO algorithms need more than 40 iterations to achieve the best-seeking effect. Taking the index of agricultural water use efficiency in Bawuer farm as an example, the index weight is greater than 60% and the utilization rate of agricultural water is more than 98%, which is closer to reality. Therefore, the evaluation results of the flood disaster resilience evaluation model proposed in this study are more accurate: WDO-PP>(adaptive genetic algorithm) AGA-PP>(adaptive particle swarm optimization algorithm)APSO-PP. In conclusion, the WDO-PP model has certain reference value for flood disaster recovery, monitoring and early warning.

Spatiotemporal patterns and risk factors concerning hepatitis B virus infections in the Beijing-Tianjin-Hebei area of China.

Beijing-Tianjin-Hebei is the largest urban agglomeration in northern China, but the spatiotemporal patterns and risk factors concerning hepatitis B virus (HBV) incidence in this area have been unclear. The present study aimed to reveal the spatiotemporal epidemiological features of HBV infection and quantify the association between HBV infection and socio-economic risk factors. The data on HBV cases in Beijing-Tianjin-Hebei from 2007 to 2012 was collected for each county. The Bayesian space-time hierarchy model and the GeoDetector method were used to reveal spatiotemporal patterns and detect risk factors. High-risk regions were mainly distributed in the underdeveloped rural areas in the north and mid-south of the study region, while low-risk regions were mainly distributed in the urban and western areas. The HBV annual incidence rate decreased substantially over the 6-year period, dropping from 7.34/105 to 5.51/105. Compared with this overall trend, 38.5% of high-risk counties showed a faster decrease, and 35.9% of high-risk counties exhibited a slower decrease. Meanwhile, 29.7% of low-risk counties had a faster decrease, and 44.6% of low-risk counties exhibited a slower decrease. Socio-economic factors were strongly associated with the spatiotemporal patterns and variation. The population density and gross domestic product per capita were negatively associated with HBV transmission, with determinant powers of 0.17 and 0.12, respectively. The proportion of primary industry and the number of healthcare workers were positively associated with the disease incidence, with determinant powers of 0.11 and 0.8, respectively. The interactive effect between population density and the other factors exerted a greater influence on HBV transmission than that of these factors measured independently.

Temporal-spatial decomposition computing of regional water intensity for Yangtze River Economic Zone in China based on LMDI model

The shortage of water resources has become a burning issue constraining China’s sustained development. Quantifying the driving effect of temporal and spatial differences of water intensity in Yangtze River Economic Zone is very important to Three Red Lines and the dual implementation action of water resources and intensity. In this study, an Index Decomposition Analysis-Logarithmic Mean Divisia Index (IDA-LMDI) model is developed to decompose the temporal-spatial differences of water intensity into intensity effect and structure effect from 2000 and 2015 in Yangtze River Economic Zone. Our findings are as follows. (1)Industrial water intensity and industrial structure are primary and secondary factors that promote the reduction of water intensity, the water intensity of the three industries has generally declined. (2)The primary industry and secondary industry contribute more to the decline of water intensity than the tertiary industry and the primary industry transfers to the secondary industry and the tertiary industry to drive the decline of water intensity. (3)The gap between the water intensity of each province and the average level of the Yangtze River Economic Zone gradually narrowed, and there are significant differences in the evolution trend of intensity effect and structural effect in each province. (4)Only water intensity of the three industries in Zhejiang is always lower than average level, nevertheless, Jiangxi, Hunan are always higher than average level, the proportion of primary industry in GDP in the Yangtze River Delta (Shanghai, Jiangsu and Zhejiang) is less than the average level. Therefore, the provinces should give consideration to the improvement of industrial water efficiency and industrial structure adjustment and other water resources policies to promote the decline of water intensity, greatly improve the effective utilization coefficient of irrigation water and reuse of industrial water. Government should increase support for the management, capital and technology of less developed provinces in the Yangtze River Economic Zone. Provinces should focus on policies that reduce the water intensity, Zhejiang and Chongqing can be a model for other provinces to learn from advanced experience, especially in Zhejiang.

Study on the Driving Mechanism of Ecosystem Service Value on Ximen Island Based on STIRPAT Model

The ecosystem service value on Ximen Island was calculated using the value table of ecosystem services proposed by Xie et al. according to the land use data from 2006–2017. The STIRPAT model was used to analyze the driving mechanisms of ecosystem services on Ximen Island. The results show that the ecosystem service value of Ximen Island has gradually decreased from 2006 to 2017, and the value of ecosystem services has decreased by 15.842 million Yuan over 10 years. Applying the principal component analysis method can effectively eliminate the collinearity problem in the process of regression analysis. The value of ecosystem services has a high correlation with socio-economic variables. The total population, GDP per capita, the increased rate of forestry output, the proportion of primary industry, and the Engel coefficient are all important driving factors that affect the change of valuable ecosystem services on Ximen Island. Among them, the proportion of primary industry is positive, and the other 4 indicators are negative. The rate of increase in forestry output has the greatest negative impact. A 1% increase in the total population, in the GDP per capita, in the rate of forestry output, in the proportion of primary industry, and in the Engel coefficient results in ecosystem service values varying by 0.199%, 0.165%, 0.289%, -0.144%, and 0.252%, respectively.

Spatio-Temporal Pattern and Risk Factor Analysis of Hand, Foot and Mouth Disease Associated with Under-Five Morbidity in the Beijing-Tianjin-Hebei Region of China.

Hand, foot and mouth disease (HFMD) in children under the age of five is a major public health issue in China. Beijing–Tianjin–Hebei is the largest urban agglomeration in northern China. The present study aimed to analyze the epidemiological features of HFMD, reveal spatial clusters, and detect risk factors in this region. Reports of HFMD cases in Beijing–Tianjin–Hebei from 1 January 2013 to 31 December 2013 were collected from 211 counties or municipal districts. First, the epidemiological features were explored, and then SaTScan analysis was carried out to detect spatial clusters of HFMD. Finally, GeoDetector and spatial paneled model were used to identify potential risk factors among the socioeconomic and meteorological variables. There were a total of 90,527 HFMD cases in the year 2013. The highest rate was in individuals aged one year, with an incidence of 24.76/103. Boys (55,168) outnumbered girls (35,359). Temporally, the incidence rose rapidly from April, peaking in June (4.08/103). Temperature, relative humidity and wind speed were positively associated with the incidence rate, while precipitation and sunshine hours had a negative association. The explanatory powers of these factors were 57%, 13%, 2%, 21% and 12%, respectively. Spatially, the highest-risk regions were located in Beijing and neighboring areas, with a relative risk (RR) value of 3.04. The proportion of primary industry was negatively associated with HFMD transmission, with an explanatory power of 32%. Gross domestic product (GDP) per capita, proportion of tertiary industry, and population density were positively associated with disease incidence, with explanatory powers of 22%, 17% and 15%, respectively. These findings may be helpful in the risk assessment of HFMD transmission and for implementing effective interventions to reduce the burden of this disease.

Exploration of an Optimal Policy for Water Resources Management Including the Introduction of Advanced Sewage Treatment Technologies in Zaozhuang City, China

Water shortage and water pollution are important factors restricting sustainable social and economic development. As a typical coal resource-exhausted city and a node city of the South-to-North Water Transfer East Route Project in China, Zaozhuang City’s water resources management faces multiple constraints such as transformation of economic development, restriction of groundwater exploitation, and improvement of water environment. In this paper, we develop a linear optimization model by input–output analysis to study water resources management with the introduction of three advanced sewage treatment technologies for pollutant treatment and reclaimed water production. The simulation results showed that from 2014 to 2020, Zaozhuang City will realize an annual GDP growth rate of 7.1% with an annual chemical oxygen demand (COD) emissions reduction rate of 5.5%. The proportion of primary industry, secondary industry, and tertiary industry would be adjusted to 5.6%, 40.8%, and 53.6%, respectively. The amount of reclaimed water supply could be increased by 91% and groundwater supply could be decreased by 6%. Based on the simulation, this model proposes a scientific reference on water resources management policies, including water environment control, water supply plan, and financial subsidy, to realize the sustainable development of economy and water resources usage.

Spatio-Temporal Pattern and Socio-Economic Factors of Bacillary Dysentery at County Level in Sichuan Province, China.

Bacillary dysentery (BD) remains a big public health problem in China. Effective spatio-temporal monitoring of BD incidence is important for successful implementation of control and prevention measures. This study aimed to examine the spatio-temporal pattern of BD and analyze socio-economic factors that may affect BD incidence in Sichuan province, China. Firstly, we used space-time scan statistic to detect the high risk spatio-temporal clusters in each year. Then, bivariate spatial correlation and Bayesian spatio-temporal model were utilized to examine the associations between the socio-economic factors and BD incidence. Spatio-temporal clusters of BD were mainly located in the northern-southern belt of the midwest area of Sichuan province. The proportion of primary industry, the proportion of rural population and the rates of BD incidence show statistically significant positive correlation. The proportion of secondary industry, proportion of tertiary Industry, number of beds in hospitals per thousand persons, medical and technical personnel per thousand persons, per capital GDP and the rate of BD incidence show statistically significant negative correlation. The best fitting spatio-temporal model showed that medical and technical personnel per thousand persons and per capital GDP were significantly negative related to the risk of BD.

Socio-economic factors of bacillary dysentery based on spatial correlation analysis in Guangxi Province, China.

BackgroundIn the past decade, bacillary dysentery was still a big public health problem in China, especially in Guangxi Province, where thousands of severe diarrhea cases occur every year.MethodsReported bacillary dysentery cases in Guangxi Province were obtained from local Centers for Diseases Prevention and Control. The 14 socio-economic indexes were selected as potential explanatory variables for the study. The spatial correlation analysis was used to explore the associations between the selected factors and bacillary dysentery incidence at county level, which was based on the software of ArcGIS10.2 and GeoDA 0.9.5i.ResultsThe proportion of primary industry, the proportion of younger than 5-year-old children in total population, the number of hospitals per thousand persons and the rates of bacillary dysentery incidence show statistically significant positive correlation. But the proportion of secondary industry, per capital GDP, per capital government revenue, rural population proportion, popularization rate of tap water in rural area, access rate to the sanitation toilets in rural, number of beds in hospitals per thousand persons, medical and technical personnel per thousand persons and the rate of bacillary dysentery incidence show statistically significant negative correlation. The socio-economic factors can be divided into four aspects, including economic development, health development, medical development and human own condition. The four aspects were not isolated from each other, but interacted with each other.

English

Based on the Ecological Footprint Model, a Sustainability Index is put forward to measure the degree of regional sustainable development. Using correlation&nbsp;and sensitivity&nbsp;analysis, this study&nbsp;explored the level of sustainable development in the Xingguo County of China from 1996 to 2005, and revealed the driving factors of human activities on sustainable development. The results indicate that the consumption footprint of biological resources in the study area shows a slow, fluctuating upward trend. The increasing ratio of ecological&nbsp;carrying&nbsp;capacity is greater than that of per capita ecological carrying capacity. The values of the sustainable index range from 0.5 to 0.6 (which means unsafe), and it shows a slowly increasing trend. This indicates that the rate of population growth is faster than that of ecological recovery and improvement, which is beyond the bearing capacity of the ecological environment. The Sustainability Index in the study area has a significantly&nbsp;positive correlation with Gross Domestic Product (GDP) urbanization level and this has a significantly&nbsp;negative&nbsp;correlation&nbsp;with the proportion of primary industry. The Sustainability Index in the study area is more sensitive to population growth, followed by a decrease in primary industries. &nbsp; Key words: Sustainability Index, ecological footprint, sensitivity analysis, regional sustainable development.

Labor income shares (LIS) in economic development: an empirical study based on China's sectoral‐level data

从产业角度对中国劳动收入占比的变化进行实证研究，可以发现，产业结构变 化和不同产业劳动收入占比以正相关性同时变化，均加剧了劳动收入占比的波动。 基于产业数据对劳动收入占比变化进行分解，可以发现：相对于1993年，1996年劳 动收入占比上升主要是由第二产业劳动收入占比提高造成的；相对于1996年，2003 年劳动收入占比下降主要与第一产业比重下降有关；2004年的情况比较特殊，这一 年劳动收入占比剧烈下降主要源于统计口径变化，造成第二、三产业劳动收入占比 大幅减少。劳动收入占比在地区之间存在巨大差异，但是随着时间的推移，该差异 在逐渐缩小，这与产业结构和产业内劳动收入占比的变化也密切相关。 关键词: 劳动收入占比 结构转型 产业间效应 产业内效应 Empirical study of sectoral‐level change in LIS in China reveals that change in industry structure is synchronous and positively correlated with LIS in different sectors of the economy, intensifying overall LIS fluctuations. Our analysis of LIS based on data from the major sectors of China's economy show that, relative to 1993, the increase in LIS in 1996 was largely due to the increased proportion of LIS in secondary industry. Relative to 1996, the fall in LIS in 2003 was closely related to the fall in the proportion of primary industry in the overall economy. The year 2004 was unusual in that it witnessed a dramatic fall in overall LIS level. The primary reason for the fall was a change in statistical definition that led to a significant reduction in LIS in the secondary and tertiary sectors. There were huge variations in LIS level among different regions. However, with the passage of time, the regional disparities are gradually being reduced. This is also closely connected to changes in industry structure and within‐sector LIS fluctuations.