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This paper reviewed data from a total of 30 Chinese provinces (including Chinese provinces and municipalities except Tibet, Hongkong, Macao, and Taiwan of China) in eastern, central, and western China, which was divided according to the geographical location, administrative division and economic development level (Fig. 1). The data used in the study were derived mainly from the statistical yearbooks (National Bureau of Statistics of China, 2001–2018a; b). When confronted with missing data, this paper calculated the average of the year before and after the missing year as a replacement value for missing data. There were only several data of control variables of some provinces missing in certain years, which had no significant impacts on the whole results. Before analysis, all indicator data were standardized and normalized using the z-score method. If the primary data are greater than the mean value, the data after standardizing will be positive; otherwise, they will be negative.
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Referring to the previous literature (Hassan and Schneider, 2016; Popescu et al., 2018; Chen et al., 2020), this study employed a MIMIC model to determine the size of informal economies in China’s 30 provinces (except Tibet, Hongkong, Macao, and Taiwan of China). Existing research believed that the tax burden, resident income, unemployment, self-employment, and government regulation were the important factors that have direct relations with the informal economies. Higher tax burdens grow the costs of formal firms and increase the likelihood that firms will engage in informal economies (Elgin and Oyvat, 2013; Igudia et al., 2016). In order to identify different influences of different types of taxes on informal economies, the tax revenue was divided into direct tax revenue and indirect tax revenue, and assumed that direct tax revenue has negative effects on the size of informal economies while indirect tax revenue has positive effects (Wang et al., 2019). Additionally, the residents with sufficient income from official economies will not work in the informal economies (Feld and Schneider, 2010). Moreover, a higher unemployment rate leads to a larger informal economy as unemployed persons take informal jobs as the survival strategy (Dell’Anno and Solomon, 2008; Bajada and Schneider, 2009). Self-employed persons are more likely to evade taxes as a result of deficient regulation systems, indicating a higher self-employment rate with a larger informal economy (Hassan and Schneider, 2016; Chen et al., 2020). Government regulation has the both positive and negative effects on informal economies (Biswas et al., 2012; Huang et al., 2019). This paper also considered the positive relationship between economic growth and informal economies, and the negative correlation between labor participation and informal economy based on previous studies (Hassan and Schneider, 2016; Chen et al., 2020).
Thus, based on the existing research and availability of data from the Chinese context, this paper established an evaluation index system for the informal economy size of GDP, including two categories and seven indicators (Table 1). The casual relationship between these indicators and the informal economy is shown in Fig. 2. This approach is consistent with the theorization of informal economies as the result of economic, social, and institutional factors.
Table 1. Evaluation index system of the size of informal economies
Variable Category Indicator Source Informal economy size (IES) Causes Tax burden / % Total tax revenue/GDP, direct tax revenue/GDP and indirect tax revenue/GDP Resident income / % (Disposable income of urban residents × non-agricultural population +
disposable income ofrural residents × agricultural population)/GDPUnemployment rate / % Registered urban unemployment rate Self-employment rate / % Number of engaged persons self-employed individuals/Total employment Government regulation / % Government consumption/GDP Indicators Growth rate of GDP / % GDP of this year/GDP of last year–1 Labor participation rate / % Total employment/number of economically active persons (aged 15–64) -
The complexity in the dynamics of informal economies leads to their intricate and various impacts on the environment. Moreover, this study can not estimate the scale of informal economies by sector and failed to indicate the relationship between different sectors of informal economies and one special pollutant. Thus, this study investigated the effects of informal economies on the environment through the overall perspective, instead of focusing on one single pollutant. As the two main pollution sources in China, water pollution has long been a major concern among the environmental issues, while air pollution has gained more attention in recent years. Thus, based on the existing research (Chen and Leizhu, 2015), this study used per capita pollution emission (PPE) to capture the whole pollution status, which was calculated with the total emissions of waste water, sulfur dioxide, and soot (dust) by the total population for each province, representing water pollution and air pollution, as the dependent variable. This paper used the following equation:
$$ {{PPE}}_{{nt}}=\left({{PE}}_{{wnt}}+{{PE}}_{{sont}}+{{PE}}_{{sdnt}}\right)/{{PD}}_{{nt}} $$ (1) where PPE is per capita pollution emission, n represents provinces, t represents years, and PEwnt, PEsont, and PEsdnt are the emission of the waste water, sulfur dioxide, and soot (dust) of province n in year t, respectively. Additionally, PDnt is the total population in province n in year t.
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As per the existing literature, this study chose five factors as control variables, all of which have been shown to have an important impact on pollution (Table 2). The proportion of built-up districts in an administrative area (BUD) was selected to represent the level of urbanization and urban construction. Growth rate of per capita GDP (GDP) was used to represent the economic development factor. The proportions of secondary industry (PSI) and tertiary industry (PTI) in GDP were used to measure structural industrial factors. Moreover, the proportion of realized foreign investments in GDP (FDI) represents the degree of globalization. Energy intensity per unit of GDP (PEI) was used to represent energy use efficiency and measure technological change.
Table 2. Descriptive statistics of variables
Variable Category Abbreviation Name description Independent variable IES Size of informal economy / % Dependent variable PPE Per capita pollution emissions / t Control variables Urbanization BUD Proportion of built-up districts in an administrative area / % Economic growth GDP Growth rate of per capita GDP / % Industrial structure PSI Proportion of secondary industry in GDP / % PTI Proportion of tertiary industry in GDP / % Globalization FDI Proportion of realized foreign investments in GDP / % Technological advance PEI Energy intensity per unit of GDP / (Tons of standard coal / 103 yuan) -
This paper adopted the Theil index to quantify regional inequality of informal economies and pollution emission in China. Theil index proposed first by Theil (1967) can decompose overall inequality (T) into between-region (TB) and within-region (TW) components, which benefits for investigating the main cause of overall inequality. The Theil index we used in this paper can be defined as:
$$T = {T_B} + {T_W} = \displaystyle\sum _{i = 1}^3{Y_i}\log \frac{{{Y_i}}}{{{N_i}/30}} + \displaystyle\sum _{i = 1}^3\left(\displaystyle\sum _{j = 1}^{30}{Y_i}\log \frac{{{Y_{ij}}}}{{1/{N_i}}}\right)$$ (2) where i represents regions, j represents provinces, Yi is the share of the informal economy size (or per capita pollution emission) of region i in the country, Ni is the amounts of provinces in region i, Yij is the share of the informal economy size (or per capita pollution emission) of province j in the region i.
Referring to the EKC model, the model we used to examine the relationship between informal economies and pollution emission was:
$$IE{S_{nt}} = {\beta _0} + {\beta _1}PP{E_{nt}} + {\beta _2}PP{E_{nt}}^2 + {\beta _3}{C_{nt}} + {\varepsilon _{nt}}$$ (3) where IES is the informal economy size, PPE is per capita pollution emission, n represents provinces, t represents years, the model treats β0 as the intercept parameter and β1, β2 and β3 as estimated parameters, Cnt is control variables, ε is the standard error term.
The ‘turning point’ of informal economy’s size ι characterizing the end of the first phase of the EKC and the beginning of the second, where pollution emissions are maximum, is given by:
$$\iota = \exp (- {\beta _1}/2{\beta _2})$$ (4) -
Fig. 3 shows the development of the informal economy’s output as a share of China’s total GDP and annual means of pollution between 2000 and 2017. The remarkable growth of China’s informal economy as a share of its total GDP can be understood by dividing it into two phases. During the first phase, between 2000 and 2006, the size of informal economies grew slowly: it went from an 11.66% share of China’s total GDP in 2000 to 12.98% in 2006. This growth is associated with the reform of state-owned firms, which led to widespread lay-offs and high rates of rural-to-urban migration. In both cases, workers sought employment in informal economies. During the second phase, beginning in 2007, the informal economies grew at a high rate, reaching to the peak as 21.83% of China’s overall GDP in 2015 and fell a little down to about 20%. This rapid growth is mainly associated with the hit of global financial crisis on China’s economy in 2008 that gave rise to the serious unemployed problem, and ongoing industrial upgrades which slowed the pace of economic growth. Overall, informal employment has become a main means of livelihood for rural migrant workers and state-owned enterprise’s laid-off workers as jobs have become harder to come by in China’s large cities.
Figure 3. The development of the informal economy size and pollution in 2000–2017 of China. Tibet, Hong Kong, Macao and Taiwan of China are not included in this paper. Meanings of PPE and IES see Table 2
The growth of informal economies in China was accompanied by increasingly severe environmental degradation. Indeed, informal economic activities characterized by the illegal elusion of fiscal and merchant laws are prone to environmental issues. As shown in Fig. 3, the per capita pollution in China increased from 34.18 to 49.29 t between 2000 and 2017. This growth, too, can be split into two phases. During the first phase, between 2000 to 2010, per capita pollution grew at a high rate: about 1.32 t per year on average. During the second phase, from 2011 onward, per capita pollution leveled out, increasing by only 0.08 t per year on average. The slow of per capita pollution in this second phase is associated with growing public awareness of environmental problems and increasing policy emphasis on environmental governance since the strategy of constructing an ecological civilization was proposed in 2012.
A geographical analysis shows that the informal economy and its accompanying pollution are distributed unevenly across China (Fig. 4). The results of Table 3 show that T and TW of informal economies in China increased from 25.992 and 25.991 in 2000 to 28.239 and 28.237 in 2017, respectively, further indicating that the inequality of informal economies in China has been enlarged during the past 18 years, and it was mainly caused by the inner-region inequality of China. The changing trend of informal economies was related to the unbalanced development of formal economies in China. Moreover, informal economies grew faster in western China than in eastern China between 2000 and 2017 (Fig. 4; Table 4). This may be associated with the expansion of modern economies in the developed eastern region, leading to more formal employment and more effective economic governance. In contrast, the change in the spatial pattern of pollution emission differs from the case of informal economies. T and TW of pollution in China increased from 30.543 and 30.522 in 2000 to 32.642 and 32.629 in 2010, and then decreased into 32.541 and 32.533 in 2017, respectively, while TB reduced from 0.020 into 0.008 between 2000 and 2017 (Table 3). These indicate that the inequality of pollution in China has been reduced. Fig. 4 and Table 4 also show varying degrees of pollution and changes in pollution in all three regions; in 2000, the eastern region had the highest levels of pollution, followed by central and then western regions, but by 2017 this order shifted so that eastern, then western, then central China had the highest levels of pollution in that order. It is instructive to point out that the differences in the spatiotemporal changes of informal economies and pollution in different regions indicates a non-linear relationship between informal economic activity and the environment, and that this relationship might be affected by geographical articulations of multiple impacting factors.
Figure 4. Spatial distributions of informal economies and pollution in 2000 and 2017 of China. Meanings of PPE and IES see Table 2; Tibet, Hong Kong, Macao and Taiwan of China are not included in this paper
Table 3. Regional differences of informal economies and pollution in 2000, 2010 and 2017 of China
Value Informal economy size Per capita pollution emission 2000 2010 2017 2000 2010 2017 T 25.992 26.565 28.239 30.543 32.642 32.541 TB 0.001 0.007 0.002 0.020 0.013 0.008 TW 25.991 26.558 28.237 30.522 32.629 32.533 Note: Meanings of T, TB, and TW see 3.3; Tibet, Hong Kong, Macao and Taiwan of China are not included in this paper Table 4. Changes in the size of informal economies and pollution in 2000 and 2017 of China
Region Informal economy size / % of GDP Per capita pollution emission / t 2000 2017 Increase 2000 2017 Increase Eastern region 12.48 20.41 7.93 47.90 62.27 14.36 Central region 10.31 16.84 6.53 29.07 41.11 12.04 Western region 11.83 22.13 10.30 24.17 42.25 18.09 Note: Tibet, Hong Kong, Macao and Taiwan of China are not included in this paper -
In addressing the research question, this study assumed that informal economies and pollution interacted. Before the cointegration tests and vector autoregression (VAR), the stationarity of the variables was tested. The results indicate that the panel data are stationary using first differences, satisfying the necessary conditions to examine whether a long-term relationship exists between informal economies and pollution further. Next, conducting the Johansen maximum-likelihood cointegration test, the results show that there is a cointegrated relationship between informal economies and pollution. After building the panel VAR model, the optimal lag length was determined to be six lags by compounding the Akaike Information Criterion and Schwarz Criterion criteria and the sample size. Using AR Roots test, the model is proved to be steady, suggesting that pollution will ineluctably be produced as the provincial informal economies grow.
The results of the Granger causality test indicate that informal economy significantly impacts regional pollution, and the opposite results also hold, as depicted in Table 5. This indicates that informal economies pollute and have negative impacts on urban environments. Moreover, with the change of the provincial pollution, informal economies are influenced accordingly and would be restrained in the long run.
Table 5. Results of the Granger causality test of informal economies and pollution in China
Dependent variable PPE Dependent variable IES Excluded Chi-sq df Prob. Excluded Chi-sq df Prob. IES 25.76447 1 0.0002 PPE 19.39863 1 0.0035 All 25.76447 1 0.0002 All 19.39863 1 0.0035 Note: Meanings of PPE and IES see Table 2; Tibet, Hong Kong, Macao and Taiwan of China are not included in this paper -
This paper tested the relationship between informal economies and pollution via regression models of the aforementioned panel data. The Hausman test showed that the fixed effects model was better than the random effects model. The respective results using the fixed effects models are presented in Table 6.
Table 6. Results of estimates by panel regression model on informal economies’ impact in China
Variables Model (1) Model (2) Model (3) IES 0.122*** 0.670*** IES2 –0.448*** BUD –0.222*** –0.291*** –0.199*** GDP 0.037** 0.052*** 0.027* PSI 0.476*** 0.435*** 0.523*** PTI 0.503*** 0.455*** 0.583*** FDI –0.084*** –0.097*** –0.096*** PEI –0.195*** –0.173*** –0.258*** R2 0.904 0.909 0.901 N 540 540 540 Note: *, ** and *** indicate rejection of the null hypothesis at the 10%, 5% and 1% significance levels, respectively; Meanings of variables see Table 2; IES2 means the quadratic term of ‘informal economy size’; Tibet, Hong Kong, Macao and Taiwan of China are not included in this paper Firstly, Model 1 confirms the significant positive impact of informal economies on provincial pollution, indicating that the growth of informal economies may lead to environmental degradation in China because actors in informal economies evade and do not follow environment regulations.
Informed by the inconclusive character of the relevant literature, this paper added a quadratic term (‘informal economy size’) in Model 2 to examine whether there is a non-linear relationship between informal economy size (IES) and the per capita pollution emission (PPE). The estimated results show that the coefficient of the quadratic term of informal economy size is negative, indicating there is a significant non-linear relationship between IES and PPE. Based on Equation (3), it is suggested that provincial environments will improve once the IES reaches the turning pointing of the informal EKC, which is 16.82% of GDP. This means that the informal economy has positive impacts on per capita pollution before its size represents a certain share of the economy, and at that point it has a negative impact. In other words, informal economies will contribute to the reduction in environmental pollution after its size takes 16.82% of GDP in Chinese context.
This paper also considered the impact of various control variables on pollution in China without the presence of the informal economy by running Model 3 (Table 6). Models 1, 2, and 3 show that GDP, PSI, and PTI have significant, positive impacts on PPE, and the influence of PTI in particular is quite large. Additionally, BUD, FDI, and PEI had significantly negative influences. These results suggest that extensive economic development came at the cost of remarkable resource consumption, which exacerbates the deterioration of the environment in China.
This paper also compared considered the informal economy’s environmental effects beyond pollution. As shown in Table 6, the coefficients of PSI, PTI, and FDI are lower in Model 1 than in Model 3. However, the effects of GDP, BUD, and PEI are larger in Model 1. This might be because, while the informal economy played a role in promoting urbanization and economic development, it may also impede the adjustment of industrial structures and technological advances and reduce their effects on environment outcomes in certain ways. Thus, the effects of urbanization and the economic development on pollution emission have been weakened, while the impacts of structural industrial adjustments, globalization, and technological improvements have been exaggerated when excluding the existence of informal economies.
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Because different regions exhibit different degrees of development and are affected by informal economies in distinct ways, they may show diverse contaminative effects. The estimated results of the fixed effects models (Table 7) used in this study confirm that informal economies in different regions exhibit different environmental effects in China.
Table 7. Informal economies’ impacts on pollution in different regions of China
Variables Eastern region Central region Western region IES 0.779*** 0.303** 0.748*** IES2 –0.510*** –0.242* –0.473*** BUD –0.388*** 3.777*** 0.605 GDP 0.023 0.059*** 0.057** PSI 0.577*** –0.027 0.417*** PTI 0.472*** –0.078 0.382*** FDI –0.118*** 0.085 –0.159* PEI –0.467*** –0.093 –0.063 R2 0.910 0.795 0.804 N 198 144 198 Notes: meanings of variables see Table 2; Tibet, Hong Kong, Macao and Taiwan of China are not included in this paper; models used here are same as Model 2 in 4.2.2 First of all, the models in Table 7 indicate that there is an inverted U-shaped relationship between informal economies and pollution in all three regions of China. This indicates that informal economies have both positive and negative environmental impacts in the long run at the regional level. This is consistent with the trend seen at the national level. However, notable differences exist in the inverted U-shaped relationship between informal economies and the environment, which is caused by different interactions of multiple factors in different regions.
Table 7 indicates that, in the highly developed eastern region of China, the inverted U-shaped relationship between informal economies and the environment is caused by the interaction of IES, BUD, PSI, PTI, FDI, and PEI, which impact environmental pollution in different ways; and the environmental effect is higher than that in the central and western regions by contrast. In these economies’ infancy, they pollute more as they grow. In this stage, it can be argued that the growths of informal economies, the secondary and tertiary industries have larger impacts and consequently play a leading role in determining rates of pollution. Additionally, the rapid development of the industries also leads to the expansion of informal economies, decreasing the production cost and increasing the environmental pollution by evading the regulation in the eastern region. However, in the later (declining) stage, urbanization, globalization, technological advances, and the size of informal economies squared have larger impacts and play a leading role in reducing pollution; leading to the view that rates of pollution decrease as informal economies grow. In this stage, one can expect that more advanced technology and high-quality FDI are used in the economy and the larger size of informal economy causes strengthened state regulation in environmental issues, which all contribute to the improvement of environmental quality.
Table 7 indicates that, in the central region, pollution increases alongside IES, BUD, and GDP. BUD’s impact is particularly large; this indicates that the expansion of urban land use is a major source of pollution in the central region of China. The increasing trend of environmental pollution is reversed by the continual growth of informal economies, which is expected to play a role in reducing pollution due to its scale effect that brings about stronger environmental regulation by the state.
In contrast, Table 7 shows that the inverted U-shaped relationship between informal economies and the environment in western China is caused by the articulation of IES, GDP, PSI, PTI, and FDI. Hence, in the climbing-up stage, as informal economies grow, they pollute more. FDI may reduce pollution because capital investment in western China is restricted to eco-friendly projects given environmental vulnerability of the region and its role as an environmentally protected area. However, differing with the eastern China, in either central or western regions, technological advances do not have a statistically significant impact on environmental quality.
The Environmental Impacts of Informal Economies in China: Inverted U-shaped Relationship and Regional Variances
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Abstract: This paper aims to the debate on the nexus between informal economies and the environment by investigating the long-term dynamic impacts of China’s informal economies on pollution and considering regional differences in informal economies’ pollution. This paper uses the Multiple Indicators Multiple Causes (MIMIC) model to estimate the size of informal economies and employs econometric models to examine their relationships to pollution based on provincial-level panel data from 2000 to 2017. The results indicate that informal economies’ effects on environmental pollution are not purely positive or negative. Rather, our model indicates that there is an inverted U-shaped relationship between informal economies and pollution in the long run in China; this means that the level of environmental pollution increases at first and then decreases with the growth of informal economies. Further analysis shows that while this inverted, U-shaped relationship is significant in different regions of China, it is affected by different environmental impact factors. The paper concludes by discussing the policy implications for environmental protection and sustainable development.
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Figure 3. The development of the informal economy size and pollution in 2000–2017 of China. Tibet, Hong Kong, Macao and Taiwan of China are not included in this paper. Meanings of PPE and IES see Table 2
Figure 4. Spatial distributions of informal economies and pollution in 2000 and 2017 of China. Meanings of PPE and IES see Table 2; Tibet, Hong Kong, Macao and Taiwan of China are not included in this paper
Table 1. Evaluation index system of the size of informal economies
Variable Category Indicator Source Informal economy size (IES) Causes Tax burden / % Total tax revenue/GDP, direct tax revenue/GDP and indirect tax revenue/GDP Resident income / % (Disposable income of urban residents × non-agricultural population +
disposable income ofrural residents × agricultural population)/GDPUnemployment rate / % Registered urban unemployment rate Self-employment rate / % Number of engaged persons self-employed individuals/Total employment Government regulation / % Government consumption/GDP Indicators Growth rate of GDP / % GDP of this year/GDP of last year–1 Labor participation rate / % Total employment/number of economically active persons (aged 15–64) Table 2. Descriptive statistics of variables
Variable Category Abbreviation Name description Independent variable IES Size of informal economy / % Dependent variable PPE Per capita pollution emissions / t Control variables Urbanization BUD Proportion of built-up districts in an administrative area / % Economic growth GDP Growth rate of per capita GDP / % Industrial structure PSI Proportion of secondary industry in GDP / % PTI Proportion of tertiary industry in GDP / % Globalization FDI Proportion of realized foreign investments in GDP / % Technological advance PEI Energy intensity per unit of GDP / (Tons of standard coal / 103 yuan) Table 3. Regional differences of informal economies and pollution in 2000, 2010 and 2017 of China
Value Informal economy size Per capita pollution emission 2000 2010 2017 2000 2010 2017 T 25.992 26.565 28.239 30.543 32.642 32.541 TB 0.001 0.007 0.002 0.020 0.013 0.008 TW 25.991 26.558 28.237 30.522 32.629 32.533 Note: Meanings of T, TB, and TW see 3.3; Tibet, Hong Kong, Macao and Taiwan of China are not included in this paper Table 4. Changes in the size of informal economies and pollution in 2000 and 2017 of China
Region Informal economy size / % of GDP Per capita pollution emission / t 2000 2017 Increase 2000 2017 Increase Eastern region 12.48 20.41 7.93 47.90 62.27 14.36 Central region 10.31 16.84 6.53 29.07 41.11 12.04 Western region 11.83 22.13 10.30 24.17 42.25 18.09 Note: Tibet, Hong Kong, Macao and Taiwan of China are not included in this paper Table 5. Results of the Granger causality test of informal economies and pollution in China
Dependent variable PPE Dependent variable IES Excluded Chi-sq df Prob. Excluded Chi-sq df Prob. IES 25.76447 1 0.0002 PPE 19.39863 1 0.0035 All 25.76447 1 0.0002 All 19.39863 1 0.0035 Note: Meanings of PPE and IES see Table 2; Tibet, Hong Kong, Macao and Taiwan of China are not included in this paper Table 6. Results of estimates by panel regression model on informal economies’ impact in China
Variables Model (1) Model (2) Model (3) IES 0.122*** 0.670*** IES2 –0.448*** BUD –0.222*** –0.291*** –0.199*** GDP 0.037** 0.052*** 0.027* PSI 0.476*** 0.435*** 0.523*** PTI 0.503*** 0.455*** 0.583*** FDI –0.084*** –0.097*** –0.096*** PEI –0.195*** –0.173*** –0.258*** R2 0.904 0.909 0.901 N 540 540 540 Note: *, ** and *** indicate rejection of the null hypothesis at the 10%, 5% and 1% significance levels, respectively; Meanings of variables see Table 2; IES2 means the quadratic term of ‘informal economy size’; Tibet, Hong Kong, Macao and Taiwan of China are not included in this paper Table 7. Informal economies’ impacts on pollution in different regions of China
Variables Eastern region Central region Western region IES 0.779*** 0.303** 0.748*** IES2 –0.510*** –0.242* –0.473*** BUD –0.388*** 3.777*** 0.605 GDP 0.023 0.059*** 0.057** PSI 0.577*** –0.027 0.417*** PTI 0.472*** –0.078 0.382*** FDI –0.118*** 0.085 –0.159* PEI –0.467*** –0.093 –0.063 R2 0.910 0.795 0.804 N 198 144 198 Notes: meanings of variables see Table 2; Tibet, Hong Kong, Macao and Taiwan of China are not included in this paper; models used here are same as Model 2 in 4.2.2 -
[1] Abid M, 2015. The close relationship between informal economic growth and carbon emissions in Tunisia since 1980: the (ir)relevance of structural breaks. Sustainable Cities and Society, 15: 11–21. doi: 10.1016/j.scs.2014.11.001 [2] Bajada C, Schneider F, 2009. Unemployment and the shadow economy in the OECD. Revue Économique, 60(5): 1033–1067. doi: 10.3917/reco.605.1033 [3] Baksi S, Bose P, 2010. Environmental regulation in the presence of an informal sector. University of Winnipeg Department of Economics Working Paper. [4] Biles J J, 2008. Informal work and livelihoods in Mexico: getting by or getting ahead? The Professional Geographer, 60(4): 541–555. doi: 10.1080/00330120802288743 [5] Biswas A K, Farzanegan M R, Thum M, 2012. Pollution, shadow economy and corruption: theory and evidence. Ecological Economics, 75: 114–125. doi: 10.1016/j.ecolecon.2012.01.007 [6] Blackman A, Bannister G J, 1998. Community pressure and clean technology in the informal sector: an econometric analysis of the adoption of propane by traditional Mexican brickmakers. Journal of Environmental Economics and Management, 35(1): 1–21. doi: 10.1006/jeem.1998.1019 [7] Blackman A, 2000. Informal sector pollution control: what policy options do we have? World Development, 28(12): 2067–2082. doi: 10.1016/S0305-750X(00)00072-3 [8] Blackman A, Shih J, Evans D et al., 2006. The benefits and costs of informal sector pollution control: Mexican brick kilns. Environment and Development Economics, 11(5): 603–627. doi: 10.1017/S1355770X06003159 [9] Brown D, McGranahan G, 2016. The urban informal economy, local inclusion and achieving a global green transformation. Habitat International, 53: 97–105. doi: 10.1016/j.habitatint.2015.11.002 [10] Castells M, Portes A, 1989. World underneath: the origins, dynamics and effects of the informal economy. In: The Informal Economy: Studies in Advanced and Less Developed Countries. Baltimore, MD, USA: The Johns Hopkins University Press, 11–37. [11] Chaudhuri S, Mukhopadhyay U, 2006. Pollution and informal sector: a theoretical analysis. Journal of Economic Integration, 21(2): 363–378. doi: 10.11130/jei.2006.21.2.363 [12] Chen H Y, Hao Y, Li J W et al., 2018. The impact of environmental regulation, shadow economy, and corruption on environmental quality: theory and empirical evidence from China. Journal of Cleaner Production, 195: 200–214. doi: 10.1016/j.jclepro.2018.05.206 [13] Chen H L, Schneider F, Sun Q L, 2020. Measuring the size of the shadow economy in 30 provinces of China over 1995-2016: the MIMIC approach. Pacific Economic Review, 25(3): 427–453. doi: 10.1111/1468-0106.12313 [14] Chen M A, 2006. Rethinking the informal economy: linkages with the formal economy and the formal regulatory environment. In: Guha-Khasnobis B, Kanbur R, Ostrom E (eds). Linking the Formal and Informal Economy: Concepts and Policies. Oxford, England: Oxford University Press, 75–92. [15] Chen Q Q, Taylor D, 2020. Economic development and pollution emissions in Singapore: Evidence in support of the Environmental Kuznets Curve hypothesis and its implications for regional sustainability. Journal of Cleaner Production, 243: 118637. doi: 10.1016/j.jclepro.2019.118637 [16] Chen Zuhai, Leizhu Jiahua, 2015. The spatial-temporal characteristics and economic drivers of environmental pollution changes in China. Geographical Research, 34(11): 2165–2178. (in Chinese) [17] Dell’Anno R, Solomon O H, 2008. Shadow economy and unemployment rate in USA: is there a structural relationship? An empirical analysis. Applied Economics, 40(19): 2537–2555. doi: 10.1080/00036840600970195 [18] Elgin C, Oyvat C, 2013. Lurking in the cities: urbanization and the informal economy. Structural Change and Economic Dynamics, 27: 36–47. doi: 10.1016/j.strueco.2013.06.003 [19] Elgin C, Oztunali O, 2014a. Institutions, informal economy, and economic development. Emerging Markets Finance and Trade, 50(4): 145–162. doi: 10.2753/REE1540-496X500409 [20] Elgin C, Oztunali O, 2014b. Environmental Kuznets curve for the informal sector of Turkey (1950–2009). Panoeconomicus, 61(4): 471–485. doi: 10.2298/PAN1404471E [21] Elgin C, Oztunali O, 2014c. Pollution and informal economy. Economic Systems, 38(3): 333–349. doi: 10.1016/j.ecosys.2013.11.002 [22] Fang Chuanglin, Wang Jing, 2013. A theoretical analysis of interactive coercing effects between urbanization and eco-environment. Chinese Geographical Science, 23(2): 147–162. doi: 10.1007/s11769-013-0602-2 [23] Feld L P, Schneider F, 2010. Survey on the shadow economy and undeclared earnings in OECD countries. German Economic Review, 11(2): 109–149. doi: 10.1111/j.1468-0475.2010.00509.x [24] Gangopadhyay P, Shankar S, 2016. Labour (im)mobility and monopsonistic exploitation of workers in the urban informal sector: lessons from a field study. Urban Studies, 53(5): 1042–1060. doi: 10.1177/0042098015571056 [25] Grossman G M, Krueger A B, 1993. Environmental impacts of a North American free trade agreement. In: The Mexico-U.S. Free Trade Agreement. Cambridge: The MIT Press, 13–56. [26] Hassan M, Schneider F, 2016. Modelling the Egyptian shadow economy: a currency demand and a MIMIC model approach. CESIFO Working Paper No. 5727. doi: 10.13140/RG.2.1.2296.6804 [27] He Y D, Lin B Q, 2019. Investigating environmental Kuznets curve from an energy intensity perspective: empirical evidence from China. Journal of Cleaner Production, 234: 1013–1022. doi: 10.1016/j.jclepro.2019.06.121 [28] Hilbrecht M, Lero D S, 2014. Self-employment and family life: constructing work-life balance when you’re ‘always on’. Community, Work & Family, 17(1): 20–42. doi: 10.1080/13668803.2013.862214 [29] Holtz-Eakin D, Selden T M, 1995. Stoking the fires? CO2 emissions and economic growth. Journal of Public Economics, 57(1): 85–101. doi: 10.1016/0047-2727(94)01449-X [30] Hove M, Ndawana E, Ndemera W S, 2020. Illegal street vending and national security in Harare, Zimbabwe. Africa Review, 12(1): 71–91. doi: 10.1080/09744053.2019.1685323 [31] Huang G Z, Zhang H O, Xue D S, 2018. Beyond unemployment: informal employment and heterogeneous motivations for participating in street vending in present-day China. Urban Studies, 55(12): 2743–2761. doi: 10.1177/0042098017722738 [32] Huang Gengzhi, Xue Desheng, Zhang Hongou, 2016a. The development of urban informal employment and its effect on urbanization in China. Geographical Research, 35(3): 442–454. (in Chinese) [33] Huang Gengzhi, Zhang Hongou, Xue Desheng et al., 2019. The inverted-U relationship between urban informal economy and urbanization in China. Economic Geography, 39(11): 76–83. (in Chinese) [34] Huang G Z, Xue D S, Guo Y et al., 2020a. Constrained voluntary informalisation: analysing motivations of self-employed migrant workers in an urban village, Guangzhou. Cities, 105: 102760. doi: 10.1016/j.cities.2020.102760 [35] Huang G Z, Xue D S, Wang B, 2020b. Integrating theories on informal economies: an examination of causes of urban informal economies in China. Sustainability, 12(7): 2738. doi: 10.3390/su12072738 [36] Huang L, Yan L J, Wu J G, 2016b. Assessing urban sustainability of Chinese megacities: 35 years after the economic reform and open-door policy. Landscape and Urban Planning, 145: 57–70. doi: 10.1016/j.landurbplan.2015.09.005 [37] Huynh C M, 2020. Shadow economy and air pollution in developing Asia: what is the role of fiscal policy? Environmental Economics and Policy Studies, 22(3): 357–381. doi: 10.1007/s10018-019-00260-8 [38] Igudia E, Ackrill R, Coleman S et al., 2016. Determinants of the informal economy of an emerging economy: a multiple indicator, multiple causes approach. International Journal of Entrepreneurship & Small Business, 28(2–3): 154–177. doi: 10.1504/ijesb.2016.076643 [39] International Labour Organization (ILO), 2012. Measuring Informality: A New Statistical Manual on the Informal Sector and Informal Employment. Geneva: ILO. [40] Imamoglu H, 2018. Is the informal economic activity a determinant of environmental quality? Environmental Science and Pollution Research, 25(29): 29078–29088. doi: 10.1007/s11356-018-2925-y [41] Kuznets S, 1955. Economic growth and income inequality. The American Economic Review, 45(1): 1–28. [42] Lahiri-Dutt K, 2004. Informality in mineral resource management in Asia: raising questions relating to community economies and sustainable development. Natural Resources Forum, 28(2): 123–132. doi: 10.1111/j.1477-8947.2004.00079.x [43] Li Jian, Zhou Hui, 2012. Correlation analysis of carbon emission intensity and industrial structure in China. China Population, Resources and Environment, 22(1): 7–14. (in Chinese) [44] Liu C, Chen L, Vanderbeck R M et al., 2018. A Chinese route to sustainability: postsocialist transitions and the construction of ecological civilization. Sustainable Development, 26(6): 741–748. doi: 10.1002/sd.1743 [45] Maloney W F, 2004. Informality revisited. World Development, 32(7): 1159–1178. doi: 10.1016/j.worlddev.2004.01.008 [46] Nasir M, Ur Rehman F, 2011. Environmental Kuznets curve for carbon emissions in Pakistan: an empirical investigation. Energy Policy, 39(3): 1857–1864. doi: 10.1016/j.enpol.2011.01.025 [47] National Bureau of Statistics of China, 2001–2018a. China Statistical Yearbook (2000–2017). Beijing: China Statistical Press. (in Chinese) [48] National Bureau of Statistics of China, 2001–2018b. China City Statistical Yearbook (2000–2017). Beijing: China Statistical Press. (in Chinese) [49] Organization for Economic Co-operation and Development (OECD), 2012. Inclusive Green Growth: for the Future We Want. Paris: OECD. [50] Ofosu G, Dittmann A, Sarpong D et al., 2020. Socio-economic and environmental implications of Artisanal and Small-scale Mining (ASM) on agriculture and livelihoods. Environmental Science & Policy, 106: 210–220. doi: 10.1016/j.envsci.2020.02.005 [51] Özgür G, Elgin C, Elveren A Y, 2021. Is informality a barrier to sustainable development? Sustainable Development, 29(1): 45–65. doi: 10.1002/sd.2130 [52] Pang J R, Mu H L, Zhang M, 2020. Interaction between shadow economy and pollution: empirical analysis based on panel data of northeast China. Environmental Science and Pollution Research, 27(17): 21353–21363. doi: 10.1007/s11356-020-08641-3 [53] Perry G E, Maloney W F, Arias O S et al., 2007. Informality: Exit and Exclusion. Washington DC: World Bank. [54] Popescu G H, Davidescu A A M, Huidumac C, 2018. Researching the main causes of the Romanian shadow economy at the micro and macro levels: implications for sustainable development. Sustainability, 10(10): 3518. doi: 10.3390/su10103518 [55] Portes A, Schauffler R, 1993. Competing perspectives on the Latin American informal sector. Population and Development Review, 19(1): 33–60. doi: 10.2307/2938384 [56] Pow C P, 2018. Building a harmonious society through greening: ecological civilization and aesthetic governmentality in China. Annals of the American Association of Geographers, 108(3): 864–883. doi: 10.1080/24694452.2017.1373626 [57] Rupasingha A, Goetz S J, Debertin D L et al., 2004. The environmental Kuznets curve for US counties: a spatial econometric analysis with extensions. Papers in Regional Science, 83(2): 407–424. doi: 10.1111/j.1435-5597.2004.tb01915.x [58] Sassen S, 1997. Informalization in Advanced Market Economies. Geneva: Issues in Development Discussion Paper 20, International Labour Office. [59] Shen H Z, Tao S, Chen Y L et al., 2017. Urbanization-induced population migration has reduced ambient PM2.5 concentrations in China. Science Advances, 3(7): e1700300. doi: 10.1126/sciadv.1700300 [60] Stern D I, Common M S, 2001. Is there an environmental Kuznets curve for sulfur? Journal of Environmental Economics and Management, 41(2): 162–178. doi: 10.1006/jeem.2000.1132 [61] Theil H, 1967. Economics and Information Theory. Amsterdam: North Holland Publishing Company. [62] Tokman V E, 2001. Integrating the informal sector in the modernization process. SAIS Review, 21(1): 45–60. doi: 10.1353/sais.2001.0027 [63] United Nations Environment Programme (UNEP), 2015. Building Inclusive Green Economies in Africa: Experience and Lessons Learned 2010–2015. Geneva: United Nations Environment Programme. [64] Wang S, Yuan Y Z, Wang H, 2019. Corruption, hidden economy and environmental pollution: a spatial econometric analysis based on China’s provincial panel data. International Journal of Environmental Research and Public Health, 16(16): 2871. doi: 10.3390/ijerph16162871 [65] Williams C C, Round J, 2010. Explaining participation in undeclared work. European Societies, 12(3): 391–418. doi: 10.1080/14616691003716910 [66] Xu B J, Zhong R Y, Hochman G et al., 2019. The environmental consequences of fossil fuels in China: national and regional perspectives. Sustainable Development, 27(5): 826–837. doi: 10.1002/sd.1943 [67] Xue Desheng, Lin Tao, Huang Gengzhi, 2014. The development of informal sectors in the external-oriented manufacturing sector in PRD, China: a case study of leather industry in Shiling Town, Guangzhou. Geographical Research, 33(4): 698–709. (in Chinese) [68] Yang J M, Tan Y M, Xue D S, 2019. The impacts of globalization on city environments in developing countries: a case study of 21 cities in Guangdong Province, China. Journal of Cleaner Production, 240: 118273. doi: 10.1016/j.jclepro.2019.118273 [69] Yang J M, Xue D S, Huang G Z, 2020. The changing factors affecting local environmental governance in China: evidence from a study of prefecture-level cities in Guangdong Province. International Journal of Environmental Research and Public Health, 17(10): 3573. doi: 10.3390/ijerph17103573 [70] Yang W Y, Li T, Cao X S, 2015. Examining the impacts of socio-economic factors, urban form and transportation development on CO2 emissions from transportation in China: a panel data analysis of China’s provinces. Habitat International, 49: 212–220. doi: 10.1016/j.habitatint.2015.05.030 [71] Zhang L, Gao J, 2016. Exploring the effects of international tourism on China’s economic growth, energy consumption and environmental pollution: evidence from a regional panel analysis. Renewable and Sustainable Energy Reviews, 53: 225–234. doi: 10.1016/j.rser.2015.08.040