CC BY
4
© Liu Xueyao, Zoryna T.G. УДК 338.27
REGIONAL POLLUTION AND THE GEOGRAPHICAL DISTRIBUTION OF POLLUTION INTENSIVE INDUSTRIES IN CHINA
Liu Xueyao1, Zoryna 2 T.G.
1Belarusian State University, Minsk, Belarus 2Institute of Energy of the National Academy of Sciences of Belarus, Minsk, Belarus
Abstract: THE PUPROSE. To learn the regional characteristics in terms of economic development and pollution generation, to identify the pollution intensive industries with consideration to the different in industrial costs arising from the green development policy, to analyze the geographical distribution of pollution intensive industries and to determine the factors influencing the geographical distribution of pollution intensive industries in China. METHODS. Two methods are proposed for identifying pollution intensive industries for countries or regions with and without sound green development policies, the two-step cluster analysis is applied to category the regions and industries, and the regression analysis is applied to determine the factors influencing the geographical distribution of pollution intensive industries. RESULTS. The article considered the influence of green development policy on the industrial costs and propose two methods for identifying the pollution-intensive industries for countries or regions with and without sound green development policies. CONCL USION. Both methods identified the same industries as pollution-intensive industries in China. And based on the identified pollution-intensive industries, the geographical distribution of pollution intensive industries and the factors influencing the geographical distribution of pollution intensive industries are analysed. The geographical distribution of pollution intensive industries is correlated with the total investment and the energy supply in factor endowment, with the industrial waste water treatment capacity and the domestic waste treatment capacity in environmental regulation, and with the available foreign capital, фthe value of export and the value of import in globalization. These factors collectively influence the geographical distribution ofpollution intensive industries in China.
Keywords: economic development; pollution generation; pollution-intensive industry; green development policy; location selection; influence factor.
For Citation: Liu Xueyao, Zoryna T.G. Regional pollution and the geographical distribution of pollution intensive industries in China. KAZAN STATE POWER ENGINEERING UNIVERSITY BULLETIN. 2024. T. 16. No. 3 (63). P. 106-119.
РЕГИОНАЛЬНОЕ ЗАГРЯЗНЕНИЕ И ГЕОГРАФИЧЕСКОЕ РАСПРЕДЕЛЕНИЕ ОТРАСЛЕЙ, ИНТЕНСИВНО ЗАГРЯЗНЯЮЩИХ ОКРУЖАЮЩУЮ СРЕДУ, В
КИТАЕ
Лю Сюэяо 1, Зорина Т.Г. 2
белорусский государственный университет, г. Минск, Беларусь 2Институт энергетики Национальной академии наук Беларуси, г. Минск, Беларусь
tanyazorina@tut. by
Резюме: ЦЕЛЬ. Изучить региональные характеристики с точки зрения экономического развития и образования загрязнения, определить отрасли с интенсивным загрязнением с учетом различий в промышленных издержках, возникающих в результате политики зеленого развития, проанализировать географическое распределение отраслей с интенсивным загрязнением и определить факторы, влияющие на географическое распределение отраслей с интенсивным загрязнением в Китае. МЕТОДЫ. Предложены два метода для определения отраслей с интенсивным загрязнением для стран или регионов с
разумной политикой зеленого развития и без нее: двухэтапный кластерный анализ применяется для категоризации регионов и отраслей, а регрессионный анализ применяется для определения факторов, влияющих на географическое распределение отраслей с интенсивным загрязнением. РЕЗУЛЬТАТЫ. В статье рассматривается влияние политики зеленого развития на промышленные издержки и предлагаются два метода для определения отраслей с интенсивным загрязнением для стран или регионов с разумной политикой зеленого развития и без нее. ЗАКЛЮЧЕНИЕ. Оба метода определили одни и те же отрасли как отрасли с интенсивным загрязнением в Китае. И на основе выявленных отраслей с интенсивным загрязнением анализируются географическое распределение отраслей с интенсивным загрязнением и факторы, влияющие на географическое распределение отраслей с интенсивным загрязнением. Географическое распределение отраслей с интенсивным загрязнением коррелирует с общими инвестициями и энергоснабжением в факторном обеспечении, с мощностью очистки промышленных сточных вод и мощностью очистки бытовых отходов в экологическом регулировании, а также с доступным иностранным капиталом, стоимостью экспорта и стоимостью импорта в глобализации. Эти факторы в совокупности влияют на географическое распределение отраслей с интенсивным загрязнением в Китае.
Ключевые слова: экономическое развитие; образование загрязнений; промышленность с интенсивным загрязнением; политика зеленого развития; выбор местоположения; фактор влияния.
Для цитирования: Лю Сюэяо, Зорина Т.Г. Региональное загрязнение и географическое распределение отраслей, интенсивно загрязняющих окружающую среду, в Китае // Вестник Казанского государственного энергетического университета. 2024. Т. 16. № 3 (63). С. 106-119.
Introduction and Literature Review (Введение и Литературный обзор)
Industrial activity is not only the main contributor to the regional economy, but also one of the main sources of regional pollution. From the perspective of modern economics, the regional economy and regional pollution are two keys determining the regional development status and development potential. There are many studies on regional economy and regional pollution. However, in China, which has a vast territory and many differences among regions, regional economy and regional pollution vary with the industrial transfer. Many studies had analysed the factors promoting industrial transfer across countries or regions. The scarcity of environment, the development stage of economy and closeness to market are considered to be important factors influencing the transfer of pollution intensive industries [1,2,3]. And some studies trying to verify the pollution haven hypothesis came to inconsistent conclusions [4,5,6]. There is still a lack of empirical studies on the geographical distribution of pollution intensive industries in China and the factors influencing the geographical distribution of pollution intensive industries in China. It is crucial to study regional economic development and pollution generation, the geographical distribution of pollution intensive industries in China and the factors influencing the geographical distribution of pollution intensive industries in China, so as to achieve sustainable development in China.
Materials and methods (Материалы и методы)
In order to learn the specific status of economic development and pollution generation in each region of China, gross regional product per capita is selected as an indicator reflecting regional economic development, while seven indicators on the generation of pollutants, such as sulphur dioxide, nitrogen oxide, particulate matter, chemical oxygen demand, ammonia nitrogen, common industrial solid wastes and hazardous industrial solid wastes are assigned weights by entropy weight method and the superimposed values is regarded as an indicator reflecting the regional pollution generation [7,8]. The formulas for calculating the pollution generation are as follows.
The raw data of each pollutant indicator need to be processed with the formula 1.
x = чг^ы (1)
xj,max xj,min
WhereX-- standardized value of indicator x- - raw value of indicator j in region i; xjmin - minimum value of indicator j in all studied regions; xjmax - maximum value of indicator j in all studied regions.
© Liu Xueyao, Zoryna T.G.
The proportions of pollutant indicators could be calculated with the formula 2.
Pij = ^T (2)
Where pj - proportion of indicator j in region i.
The entropies for pollutant indicators could be obtained with the formula 3.
ej = -ky 2 "= ! \Pij*ln M ] (3)
Where ej - entropy for indicator j.
The coefficients of variation of pollutant indicators could be calculated with the formula 4.
Aj = l- ej (4)
Where Aj - coefficient of variation of indicator j.
The weights for pollutant indicators could be obtained with the formula 5.
wj = i^T (5)
Where wj - weight for pollutant indicator j.
And the pollution generation could be calculated with the formula 6.
^ = ±(w j * X ¿j) (6)
Where ut - pollution generation in region i.
Based on the above methodology, the pollution generation by region in China can be calculated. Our study on the economic development and pollution generation by region in China is conducted with the average value of gross regional product per capita and pollution generation by region from 2018 to 2022. And through two-step cluster, the regions of China could be classified into four groups with different characteristics (fig. 1). The characteristics conclude developed economy with slight pollution generation, developing economy with slight pollution generation, developing economy with severe pollution generation and developed economy with severe pollution generation. The reason for adopting the two-step cluster to conduct the study is that the differences in the means of each group given by the two-step cluster are more significant. And the two-step cluster analysis is realized through the software SPSS.
Developing n <f cluster 5.94 (ЮОООуиап) "
—gross regional product per capita -
Developed n of duster !2 42{10Ш?Шл}
Developing economy w ith Developed economy with
severe pollution generation severe pollution generation
Hebei Jiangsu
Shanxi Zhejiang
Inner Mongolia Fujian
Liaoning Guangdong
1 Icilongjiang
Anhui
Jiangxi
Shandong
Henan
Hubei
Hunan
Guangxi
Sichuan
Guizhou
Yunnan
Shaanxi
Xinjiang
Developing economy with Developed economy with
slight pollution generation slight pollution generation
Jilin Beijing
Hainan Tianjin
Chongqing Shanghai
Gansu
Qinghai
Ningxia
Рис. 1. Матрица «валовой региональный Fig. 1. Matrix of "gross regional product per продукт на душу населения - образование capita - pollution generation" of Chinese regions загрязнений» регионов Китая
*Источник: Составлено авторами Source: compiled by the author.
As can be seen from the figure, some regions in China have achieved developed economy with slight pollution generation. These regions are important regions regarded as political centre and economic centre of China. And there are some regions with developed economy and severe pollution generation. These regions are located along with the south-eastern coastline and are the important regions to which the national economic development strategy always pays attention. They have advantages in many aspects. Besides, it is evident that the economic development is unbalanced across regions in China and there are a large proportion of regions having
unsatisfactory economy. Among the regions with developing economy, some have slight pollution generation, while others have severe pollution generation. The regions with energy endowment are all included in the category characterized by developing economy with severe pollution generation.
In the world today, pollution comes mainly from the industrial activities and the residential life in highly industrialized societies. As for China, industrial development is still the primary approach to ensure stable economic development. Among the existing industries, some industrial sectors show high pollution characteristics. Along with their production activities, large amounts of pollution are generated and could be discharged directly into the local ecosystem, affecting the regional pollution level and causing environmental stress. Therefore, the geographical distribution of pollution-intensive industries influences the regional pollution. In order to identify pollution-intensive industries, the comprehensive pollution intensity index by industry should be calculated.
With regard to industry or the enterprises constituting industry, the primary concern is the profit derived from industrial activities. The profit generated from industrial activity can be used not only to develop and expand the industry, but also to invest in innovative technologies and equipment for green development. Therefore, industrial profit is critical both for the industry itself and for green development. However, in certain periods there could be industries with negative profit in certain countries and regions. Such situations may occur in countries or regions where green development policies are not well developed. In these countries and regions, there is a lack of relevant policies providing positive feedback to industries on their green behaviours, resulting in low income but high costs for enterprises implementing pollution control. The high costs become a major constraint for industries to control pollution and protect environment. In this case, some industries may have negative profit. Therefore, when we calculate the comprehensive pollution intensity index by industry, it is necessary to take into account the differences in industrial costs caused by green development policies, including emissions trading and pollution taxes. In countries or regions with sound green development policies, there should be profit above zero for all industries over a certain period. The comprehensive pollution intensity index by industry in such countries or regions will be calculated based on industrial profit, as shown in the formulae 7-11 [9].
,Pi>0 (7)
Where ly - discharge intensity of pollutant j in industry i; Ey - emission of pollutant j in industry i, ton sulphur dioxide emission, ton nitrogen oxide emission, ton particulate matter emission, ton chemical oxygen demand discharged, ton ammonia nitrogen discharged, 10000 tons common industrial solid waste generated and 10000 tons hazardous industrial solid wastes generated; Pt - total profit of industry i, billion yuan.
I'a = , 'iJ~'JTln (8)
Where I'y- standardized value of discharge intensity of pollutant j in industry i; ly - raw value of discharge intensity of pollutant j in industry i; lymin - minimum value of discharge intensity of pollutant j in all studied industries; lymmax - maximum value of discharge intensity of pollutant j in all studied industries.
S4 = ~E~ (9)
Where Sy - discharge scale of pollutant j in industry i; Ey - emission of pollutant j in industry i, ton sulphur dioxide emission, ton nitrogen oxide emission, ton particulate matter emission, ton chemical oxygen demand discharged, ton ammonia nitrogen discharged, 10000 tons common industrial solid waste generated and 10000 tons hazardous industrial solid wastes generated; Ey- total emission of pollutant j in all studied industries, ton sulphur dioxide emission, ton nitrogen oxide emission, ton particulate matter emission, ton chemical oxygen demand discharged, ton ammonia nitrogen discharged, 10000 tons common industrial solid waste generated and 10000 tons hazardous industrial solid wastes generated.
s'ij = sSii~Sj^in (10)
■-1 /.max ^j,min
Where S'y - standardized value of discharge scale of pollutant j in industry i; Sy - raw value of discharge scale of pollutant j in industry i; Symin - minimum value of discharge scale of pollutant j in all studied industries; SJ]max - maximum value of discharge scale of pollutant j in all studied industries.
IN Tj=I' iS*S' ц (11)
Where INTy - the pollution intensity index of pollutant j in industry i.
In countries or regions without sound green development policies, there could be profit below zero for some industries over a certain period. The comprehensive pollution intensity index by industry in such countries or regions will be calculated based on industrial income, as shown in the formula 12 and 8-11.
I := ^ .M^O (12)
ч Mt 1 v '
Where liy - discharge intensity of pollutant j in industry i; Eiy - emission of pollutant j in industry i, ton sulphur dioxide emission, ton nitrogen oxide emission, ton particulate matter emission, ton chemical oxygen demand discharged, ton ammonia nitrogen discharged, 10000 tons common industrial solid waste generated and 10000 tons hazardous industrial solid wastes generated; Mi - total income of industry i, billion yuan.
The ly based on total income calculated with formula 12 and formulae 8-11 are applied to calculate the pollution intensity index based on income.
And the comprehensive pollution intensity index by industry is obtained based on the arithmetic average value of the pollution intensity index of each studied pollutant by industry [10].
Although the profit of all industries in China are above zero for a long period of time, in order to verify the reliability of the two methods mentioned above, the relevant data of 41 industries in China are input into the formulae of the two methods to calculate and obtain the comprehensive pollution intensity index based on profit and based on income by industry respectively. Other indicators involved in the calculation of the comprehensive pollution intensity index by industry include sulphur dioxide emission, nitrogen oxide emission, particulate matter emission, chemical oxygen demand discharge, ammonia nitrogen discharged, common industrial solid wastes generated, hazardous industrial solid wastes generated. The studied period of data is from 2018 to 2022. With the obtained comprehensive pollution intensity index based on profit and based on income by industry, two-step cluster analysis is applied to categorise the studied industries. The reason for adopting the two-step cluster to conduct the study is that the differences in the means of each group given by the two-step cluster are more significant, compared to other cluster analysis methods. And the two-step cluster analysis is realized through the software SPSS.
Results (Результаты)
With the cluster analysis based on the average value of comprehensive pollution intensity index based on profit and based on income by industry from 2018 to 2022, the categories are classified as super pollution, high pollution, medium pollution and low pollution (table 1). And The industries characterized by super pollution and high pollution are identified as pollution-intensive industries in China at this stage. The results in accordance with comprehensive pollution intensity index based on profit and based on income by industry identified the same industries as pollution-intensive industries.
Table 1 Таблица 1
Average value of comprehensive pollution intensity index by industry in China from 2018 to 2022 Среднее значение комплексного индекса интенсивности загрязнения по отраслям в Китае с 2018 по
2022 гг.
Category Industry Comprehensive pollution intensity index based on profit Industry Comprehensive pollution intensity index based on income
Super pollution Production and supply of electric power and heat power 0.23882 Manufacture of non-metallic mineral products 0.27050
Smelting and pressing of ferrous metals 0.20809 Production and supply of electric power and heat power 0.23710
High pollution Manufacture of non-metallic mineral products 0.17906 Mining and processing of non-ferrous metal ores 0.18438
Manufacture of paper and paper products 0.15698 Manufacture of paper and paper products 0.16155
Mining and processing of non-ferrous metal ores 0.13372 Manufacture of raw chemical materials and chemical products 0.13572
Manufacture of raw 0.12768 Smelting and pressing of 0.11951
chemical materials and ferrous metals
chemical products
Manufacture of textile 0.12375 Manufacture of textile 0.10937
Medium Processing of food from 0.09433 Processing of food from 0.09104
pollution agricultural products agricultural products
Smelting and pressing of 0.08702 Mining and processing of 0.07260
non-ferrous metals ferrous metal ores
Mining and processing of 0.08561 Manufacture of foods 0.05668
ferrous metal ores
Processing of petroleum, 0.05276 Smelting and pressing of 0.05343
coal and other fuels non-ferrous metals
Manufacture of foods 0.03609 Mining and washing of coal 0.05102
Mining and washing of coal 0.03590 Manufacture of wine, drinks and refined tea 0.04187
Manufacture of chemical 0.03306
fibers
Low Manufacture of wine, drinks 0.01559 Processing of petroleum, 0.02641
pollution and refined tea coal and other fuels
Mining and processing of 0.01344 Manufacture of chemical 0.02356
non-metal ores fibers
Manufacture of metal 0.00727 Mining and processing of 0.01618
products non-metal ores
Manufacture of medicines 0.00663 Manufacture of medicines 0.01386
Manufacture of computers, 0.00656 Production and supply of 0.01143
communication, and other water
electronic equipment
Production and supply of 0.00598 Extraction of petroleum 0.00382
water and natural gas
Extraction of petroleum and 0.00528 Manufacture of 0.00371
natural gas computers, communication, and other electronic equipment
Manufacture of leather, fur, 0.00302 Manufacture of leather, 0.00323
feather and related products fur, feather and related
and footwear products and footwear
Processing of timber, 0.00228 Manufacture of metal 0.00317
manufacture of wood, products
bamboo, rattan, palm and
straw products
Utilization of waste 0.00223 Processing of timber, 0.00142
resources manufacture of wood, bamboo, rattan, palm and straw products
Manufacture of electrical 0.00101 Utilization of waste 0.00090
machinery and equipment resources
Manufacture of rubber and 0.00079 Manufacture of textile 0.00062
plastic wearing and apparel
Manufacture of textile 0.00072 Manufacture of electrical 0.00054
wearing and apparel machinery and equipment
Manufacture of automobile 0.00066 Manufacture of rubber and plastic 0.00046
Manufacture of 0.00054 Manufacture of railway, 0.00031
general-purpose machinery shipbuilding, aerospace and other transportation equipment
Manufacture of railway, 0.00052 Manufacture of 0.00022
shipbuilding, aerospace and automobile
other transportation
equipment
Professional and support 0.00034 Manufacture of 0.00022
activities for mining general-purpose machinery
Manufacture of furniture 0.00032 Manufacture of furniture 0.00020
Metal products, machinery 0.00031 Metal products, machinery 0.00017
and equipment repair and equipment repair
Manufacture of special 0.00023 Other manufactures 0.00013
purpose machinery
Other manufactures 0.00017 Manufacture of special purpose machinery 0.00009
Mining of other ores 0.00007 Mining of other ores 0.00004
Manufacture of tabaco 0.00007 Manufacture of tobaccos 0.00004
Manufacture of articles for 0.00006 Printing, reproduction of 0.00003
culture, education, art and recording media
crafts, sport and
entertainment activities
Printing, reproduction of 0.00006 Production and supply of 0.00003
recording media gas
Production and supply of gas 0.00006 Professional and support activities for mining 0.00003
Manufacture of measuring 0.00002 Manufacture of articles for 0.00002
instrument culture, education, art and crafts, sport and entertainment activities
Manufacture of measuring 0.00001
instrument
*Источник: Составлено авторами Source: compiled by the author.
There has been an evolution of development pattern from resource-consuming to productivity-driven along with the industrialization in countries around the world. The evolution of development pattern manifests itself geospatially in the gradient transfer of industrial structure. During this process, regional pollution generation varies with the location selection of industrial enterprises. The geographical preference of pollution-intensive industries determines the regional pollution status in the future and the possibility of green development. Therefore, the factors influencing the geographical distribution of pollution-intensive industries have become an important research topic in many countries. On this basis, many theories and hypotheses are introduced, such as factor endowment hypothesis, pollution haven hypothesis and the conjecture that globalization influencing the geographical distribution of pollution-intensive industries. In order to learn the geographical preference of pollution-intensive industries in China, the average value of the capital of pollution-intensive industries, the population employed in pollution-intensive industries and the number of enterprises in pollution-intensive industries by region in China from 2018 to 2022 are analysed (fig. 2-4), [11].
From the perspective of the capital of pollution-intensive industries, the geographical distribution of pollution-intensive industries shows unbalanced characteristics. The capital of pollution-intensive industries intends to concentrate in the developed regions along with south-eastern coastline having the advantage in the regional economy and international trade, such as Jiangsu, Guangdong and Zhejiang and to concentrate in the developing regions with a huge population as labour, more than 50 million regional people according to the data from 2018 to 2022, such as Shandong, Hebei and Sichuan. It can be tentatively seen that the geographical distribution of pollution-intensive industries is consistent with the factor endowment hypothesis, pollution haven hypothesis and the conjecture, that globalization could influence the geographical distribution of pollution-intensive industries.
0-1000 billion yuan 1000-1800 billion yuan I 1800-2600 billion yuan I 2600 billion yuan
Fig. 2. Capital of pollution-intensive industries by region from 2018 to 2022 Рис. 2. Капитал отраслей с высоким уровнем загрязнения по регионам с 2018 по 2022 гг. *Источник: Составлено авторами Source: compiled by the author.
From the perspective of the population employed in pollution-intensive industries, there exist clear tendency and characteristics in the geographical distribution of pollution-intensive industries. Similar to the analysis results from the perspective of the capital of pollution-intensive industries, the analysis on the population employed in pollution-intensive industries by region conforms that there is a relatively large number of people employed in the pollution-intensive industries in the developed regions along with south-eastern coastline having the advantage in regional economy and international trade, such as Jiangsu, Guangdong, Zhejiang and Fujian and in the regions with huge population as labour, more than 50 million regional people according to the data from 2018 to 2022, such as Shandong, Henan, Hebei and Hubei. From both perspectives of capital and employee, it proves that pollution-intensive industries intend to concentrate in the similar regions with certain characteristics. These regions are preferred in the location selection of pollution-intensive industries. The analysis on the population employed in pollution-intensive industries by region proves from another aspect that the geographical distribution of pollution-intensive industries in China at the present stage is consistent with the factor endowment hypothesis, pollution haven hypothesis and the conjecture, that globalization could influence the geographical distribution of pollution-intensive industries.
0-0.4 million people 0.4-0.8 million people I 0.8-1.2 million people I 1.2 + million people
Fig. 3. Population employed in pollution-intensive industries by region from 2018 to 2022 Рис. 3. Численность населения, занятого в отраслях с высоким уровнем загрязнения окружающей
среды, по регионам с 2018 по 2022 гг. *Источник: Составлено авторами Source: compiled by the author.
From the perspective of the number of enterprises in pollution-intensive industries, the geographical distribution of pollution-intensive industries is similar to the analysis results from the perspectives of the capital of pollution-intensive industries and the population employed in pollution-intensive industries. The number of enterprises in pollution-intensive industry varies considerably across regions. The pollution-intensive enterprises concentrate in the developed regions along with the south-eastern coastline, such as Jiangsu, Zhejiang, Guangdong and Fujian. These regions have advantages not only in the economic and social base, but also in the transportation and connection with other countries. And there shows a geographical concentration of pollution-intensive industries in developing regions with a huge population as labour, more than 50 million regional people according to the data from 2018 to 2022, such as Shandong, Henan, Sichuan, Anhui, Hebei and Hunan.
To further explore the specific factors influencing the geographical distribution of pollution-intensive industries in China at this stage, regression models are applied. Given that pollution-intensive industries could have high requirements for capital, resources, technology and labour, are constrained by environmental regulation, and are influenced by transportation and connection with other countries in the context of globalization, the variables concerned with the factor endowments, the environmental regulation and the globalization are introduced as independent variables in the regression model [12,13]. And the number of enterprises in pollution-intensive industries by region is selected as the dependent variable. The specific variables input into the regression models and their explanations are escribed as follows [14,15].
Fig. 4. Number of enterprises in pollution-intensive industries by region from 2018 to 2022 Рис. 4. Количество предприятий в отраслях с высоким уровнем загрязнения по регионам в 2018-2022 гг
*Источник: Составлено авторами Source: compiled by the author.
Factor endowment. The capital, resource, technology and labour elements are chosen to measure the effect of factor endowment on the location selection of pollution-intensive industries. Among them, the capital element is represented by the total investment to the region. The resource element is represented by the energy supply in the region. The technology element is represented by the total factor productivity of the region. And the labour element is represented by the average wage.
Environmental regulation. In China, the environmental standards and requirements are always set out by the centre government. For the regions having satisfactory pollution management capacity and where the pollutants generated can be fully treated, there is no need to propose more pollution limit and emission requirements. And the constraint on water pollution, air pollution and solid waste are all considered in the study. Therefore, the industrial waste water treatment capacity, the industrial waste gas treatment capacity, the industrial solid waste utilization
0-2000 enterprises 2000-4000 enterprises 4000-8000 enterprises 8000 + enterprises
rate and the domestic waste treatment capacity are selected to reflect the environmental regulation by region.
Globalization. In the process of globalization, the transportation costs of international trade and the occupation of international markets are important factors influencing the location selection of pollution-intensive industries. Therefore, the distance to the port, the available foreign capital, the value of export and the value of import are selected to reflect the level of globalization.
Based on the relevant data from 2018 to 2022, the regression analysis is conducted to verify the correlation between factor endowment, environmental regulation, globalization and the number of enterprises in pollution-intensive industries (table 2-4).
Table 2 Таблица 2
Correlation between factor endowment and the number of enterprises in pollution-intensive industries Корреляция между обеспеченностью факторами производства и числом предприятий в отраслях с
высоким уровнем загрязнения
Model: R R Squar e Adjusted R Square Std. Error of the Estimate Durbin-Watson F Sig.
Factor endow ment 0.920a 0.847 0.822 1413.44756 1.803 34.514 <0.0 01b
Coeffic ients Unstandardized Coefficients Standardized Coefficients Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF
(Constant) -3393. 290 1311.490 0.016
Total investment 0.119 0.022 0.620 <0.001 0.461 2.16 8
Energy supply 0.116 0.037 0.348 0.004 0.496 2.01 7
Total factor productivity 261.4 40 1404.778 0.018 0.854 0.624 1.60 2
Average wage 0.032 0.027 0.111 0.249 0.694 1.44 0
a Dependent Variable: Number of enterprises in pollution-intensive industries
*Источник: Составлено авторами Source: compiled by the author.
According to the table, it could be learned that the linear regression model between factor endowment and the number of enterprises in pollution-intensive industries hold true. The factor endowment could legitimately explain the location selection of pollution-intensive industries. The number of enterprises in pollution-intensive industries is positive correlation with the total investment and the energy supply. Holding all else constant, an increase in regional total investment or regional energy supply could lead to an increase of enterprises in pollution-intensive industries in the region. Conversely, a decrease in regional total investment or regional energy supply could bring about a departure of pollution-intensive industries from the region. The influence of total investment and energy supply on the location selection of pollution-intensive industries could be responsible for variations in pollution generation in several regions.
Table 3 Таблица 3
Correlation between environmental regulation and the number of enterprises in pollution-intensive industries Корреляция между экологическим регулированием и количеством предприятий в отраслях с высоким
уровнем загрязнения
Model: R R Squa re Adjusted R Square Std. Error of the Estimate Durbin-Watson F Sig.
Environ mental regulati on 0.913a 0.833 0.806 1476.45857 1.659 31.109 <0.0 01b
Coeffici Unstandardized Standardized Sig. Collinearity
ents Coefficients Coefficients Statistics
B Std. Error Beta Tolerance VIF
(Constant) -247 8.226 1034.170 0.024
Industrial waste water treatment capacity 2.226 0.761 0.340 0.007 0.495 2.02 1
Industrial waste gas treatment capacity 0.002 0.002 0.122 0.259 0.599 1.67 0
Industrial solid waste utilization rate 3009. 926 1681.194 0.172 0.086 0.725 1.37 8
Domestic waste treatment capacity 0.064 0.013 0.537 <0.001 0.576 1.73 7
a Dependent Variable: Number of enterprises in pollution-intensive industries
*Источник: Составлено авторами Source: compiled by the author.
According to the table, the linear regression model between environmental regulation and the number of enterprises in pollution-intensive industries holds true. The environmental regulation could explain to a great extent the location selection of pollution-intensive industries. The industrial waste water treatment capacity and the domestic waste treatment capacity have notable correlation with the number of enterprises in pollution-intensive industries. And the correlations between them are all positive. It means that, holding all other things constant, an increase in regional industrial waste water treatment capacity or regional domestic waste treatment capacity could encourage the aggregation of pollution-intensive enterprises in the region. As discussed previously, the regions have satisfactory pollution management capacity can fully solve the pollutants generated and there is no need for more environmental regulation. It causes the geographical preference of enterprises in pollution-intensive industries.
Table 4 Таблица 4
Correlation between globalization and the number of enterprises in pollution-intensive industries Корреляция между глобализацией и количеством предприятий в отраслях с высоким уровнем _загрязнения_
Model: R R Squar e Adjusted R Square Std. Error of the Estimate Durbin-Watson F Sig.
Globaliz ation 0.919a 0.844 0.820 1423.49369 2.064 33.941 <0.0 01b
Coeffici ents Unstandardized Coefficients Standardized Coefficients Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF
(Constant) 1867. 711 548.270 0.002
Distance to the port -0.39 8 0.766 -0.047 0.608 0.761 1.31 4
Available foreign capital 15.73 5 3.391 0.498 <0.001 0.540 1.85 3
Value of export 0.298 0.057 1.019 <0.001 0.166 6.01 3
Value of import -0.27 2 0.079 -0.640 0.002 0.182 5.50 1
a Dependent Variable: Number of enterprises in pollution-intensive industries
*Источник: Составлено авторами Source: compiled by the author.
According to the table, the linear regression model between globalization and the number of enterprises in pollution-intensive industries holds true. Globalization can significantly influence the location selection of pollution-intensive industries. And the location selection of pollution-intensive industries is positively correlated with the available foreign capital and the value of export and is negatively correlated with the value of import included in globalization. The regions with larger available foreign capital, larger value of export or smaller value of import are more attractive for pollution-intensive enterprises to settle in. It indicates that certain
pollution-intensive industries in China are engaged in frequent international trade. The pollution generated in certain regions may not come from internal industrial needs, but may come from pollution liabilities evaded by others [16]. These regions act as purely economic pursuers without regard for the environmental problems caused by pollution. In contrast to the available foreign capital and the value of exports, there is a negative correlation between the value of imports and the number of enterprises in pollution-intensive industries. Import taxes lead to high prices for imported goods, and such goods have comparative advantages only in economically developed regions with few industrial enterprises. The destinations of goods imported into China tend to be regions with these characteristics. It is a possible reason for the negative correlation between the value of imports and the number of enterprises in pollution-intensive industries.
Conclusions (Заключение)
By analysing the gross regional product per capita and pollution generation by region in Chine from 2018 to 2022, the general characteristics of regions are learned and the regions can be classified into four groups with different characteristics. In consideration of the differences in industrial costs arising from green development policies, different methods for identifying pollution-intensive industries are proposed for countries or regions with and without sound green development policies. Both methods identified the same industries as pollution-intensive industries in China. The characteristics of the geographical distribution of pollution-intensive industries in China from 2018 to 2022 are studied. And with the regression model, the possible factors influencing the geographical distribution of pollution-intensive industries in China from 2018 to 2022 are further verified. The results confirm that the geographical distribution of pollution-intensive industries is significantly correlated with the total investment and the energy supply in factor endowment, with the industrial waste water treatment capacity and the domestic waste treatment capacity in environmental regulation, and with the available foreign capital, the value of export and the value of import in globalization.
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Authors of the publication
Liu Xueyao — PhD student of the Department of International management, Faculty of Economics, Belarusian State University, Minsk, Republic of Belarus. ORCID: https://orcid.org/0000-0001-7510-9017. Email: 18215686524@163. com.
Tatsiana G. Zoryna — Doctor in Economics, Professor, Head of the Energy Economics Sector, Institute of Power Engineering, National Academy of Sciences of Belarus, Minsk, Republic of Belarus. ORCID: http://orcid.ors/0000-0001 -9665-2756. Email: [email protected].
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Авторы публикации
Лю Сюэяо — аспирант кафедры международного менеджмента экономического факультета
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Белорусского государственного университета, Минск, Республика Беларусь. ORCID: https://orcid.org/0000-0001-7510-9017. Email: [email protected].
Зорина Татьяна Геннадьевна — д-р экон. наук, профессор, заведующий сектором экономики энергетики Института энергетики Национальной академии наук Беларуси, Минск, Республика Беларусь. ORCID: http://orcid.org/0000-0001-9665-2756. Email: [email protected].
Шифр научной специальности: 5.2.3. «Региональная и отраслевая экономика» Получено 10.08.2024 г.
Отредактировано 26.08.2024 г.
Принято 01.09.2024 г.
