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Causal complexity of environmental pollution in China: A province-level fuzzy-set qualitative comparative analysis

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Environmental problems are endowed with the causal complexity of multiple factors. Traditional quantitative research on the influencing mechanism of environmental pollution has tended to focus on the marginal effects of specific influencing factors but generally neglected the multiple interaction effects between factors (especially three or more). Based on the panel data of 30 Chinese provinces between 2011 and 2017, this study employs fuzzy set qualitative comparative analysis (fsQCA) - which can provide a fine-grained insight into causal complexity of environmental issues - to shed light on the influencing mechanism of environmental pollution. The results show that there are several different configurations of pollution drivers which lead to high pollution or low pollution in provinces, confirming the multiple causality, causal asymmetry, and equifinality of environmental pollution. Further, the combination effect of advanced industrial structure, small population size, and technological advance is significant in achieving a state of green environment compared to environmental regulation factors. In addition, spatiotemporal analysis of the configurations indicates that strong path dependencies and spatial agglomeration exist in current local environmental governance patterns. Finally, according to our findings, targeted policy recommendations are provided.
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Causal complexity of environmental pollution in China: A province-level fuzzy-
set qualitative comparative analysis
Yang Chen ( )
Chongqing University
Jingke Hong
Chongqing University
Miaohan Tang
Chongqing University
Yuxi Zheng
Chongqing University
Maoyue Qiu
Chongqing University
Danfei Ni
Xiamen University
Research Article
Keywords: Fuzzy set, conguration, causal complexity, environmental pollution, environmental governance, QCA
Posted Date: April 27th, 2022
License: This work is licensed under a Creative Commons Attribution 4.0 International License.  Read Full License
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Environmental problems are endowed with the causal complexity of multiple factors. Traditional quantitative research on the inuencing mechanism of
environmental pollution has tended to focus on the marginal effects of specic inuencing factors but generally neglected the multiple interaction effects
between factors (especially three or more). Based on the panel data of 30 Chinese provinces between 2011 and 2017, this study employs fuzzy set qualitative
comparative analysis (fsQCA) - which can provide a ne-grained insight into causal complexity of environmental issues - to shed light on the inuencing
mechanism of environmental pollution. The results show that there are several different congurations of pollution drivers which lead to high pollution or low
pollution in provinces, conrming the multiple causality, causal asymmetry, and equinality of environmental pollution. Further, the combination effect of
advanced industrial structure, small population size, and technological advance is signicant in achieving a state of green environment compared to
environmental regulation factors. In addition, spatiotemporal analysis of the congurations indicates that strong path dependencies and spatial
agglomeration exist in current local environmental governance patterns. Finally, according to our ndings, targeted policy recommendations are provided.
1. Introduction
The rapid development of the Chinese economy has brought about serious environmental consequences(Huang et al., 2020). During the period from 2010 to
2015, China contributed nearly 20% of global emissions of nitrogen oxides (NOx) and 30% of sulfur dioxide (SO2)(Zhang et al., 2018a). In 2016, the
concentrations of PM2.5 in three-quarters of monitored cities in China were below the national grade II standard (35μg/m3) and the WHO standard
(10μg/m3)(MEP, 2017). Environmental harm poses a major threat to sustainable development, and currently attracts extensive attention from the Chinese
government(Guan et al., 2014). Moreover, the multifactorial complexity of environmental problems poses a great challenge to the traditional instruments of
environmental governance patterns(Tan and Fan, 2019). From a panoramic point of view, therefore, identifying and measuring multiple synergy effects
induced by the environmental complexity will be of great signicance to the environmental governance.
A considerable amount of existent research has attempted to analyze the main factors that inuence environmental pollution from a socioeconomic
perspective. Ehrlich and Holdren (1971) rstly attributed the anthropocentric impact (I) on the environment to population growth (P), auence development
(A), and technological progress (T) in his IPAT model. Specically, population dynamics have been at the center of arguments pertaining to environmental
deterioration (Pham et al., 2020). Based on neoclassical growth theory, a rapid expansion of population, the consumption of limited natural resources by the
existing population, and a compound of both could give rise to environmental pollution from the consumption side(Li et al., 2019). Similarly, the intertwined
connections between economic activities and environmental impacts are undeniably signicant(Guan et al., 2014). Economic growth undoubtedly stimulates
demand for natural resource extraction and consumption, and leads to environmental unsustainability(Krueger, 1995). In addition, technological factors play
a crucial part in maintaining or altering the balance between population, economy, and the environment. Unlike population growth and economic development,
technological advances through declining natural resource consumption per unit output seem to have positive effects on environmental sustainability(Pham
et al., 2020).
A further strand of literature has focused on the environmental impacts of other socioeconomic factors, including industrial structure(Zheng,
2020),urbanization(Zhang, 2018), industrial agglomeration(Shen and Peng, 2021), foreign direct investment (FDI)(Cheng et al., 2020), foreign trade(Chen et
al., 2019), and energy prices(Li et al., 2020). For instance, Zheng (2020) argued that industrial structure determines the allocation of production factors (such
as capital, labor, technology, and energy) among dierent sectors, and this, consequently, signicantly affects resource consumption and pollutant emissions.
As an important bond between environment and economy, the industrial structure is an indispensable element to realize integrative development. Zhang
(2018) proved that urbanization had a signicantly positive spatial spillover effect on CO2 emissions in China by utilizing the Spatial Durbin Panel model.
Shen and Peng (2021) conducted a spatial panel analysis of China’s environmental eciency and found an apparent U-curved relationship between industrial
agglomeration and environmental eciency. Of further note is the work of Cheng et al. (2020), who used the generalized moments method (GMM) to explore
the inuence of FDI on the environment based on the panel data of 285 Chinese cities; they asserted that FDI signicantly intensied China’s urban PM2.5
Aside from socioeconomic factors, governmental intervention, which holds a signicant role through institutional and regulatory aspects, has also attracted
considerable attention in the environmental elds. On the one hand, the government has attempted to facilitate corporate green innovation through carrot-and-
stick policies including assistances that are in the shape of green R&D subsidies(Bai, 2019), tax preferences for low-emission and high-tech
enterprises(Zheng and Shi, 2017), and punitive taxes imposed upon technologies or actions that are environmentally undesirable(Hunt and Fund, 2016).
These stimulus policies have contributed to pushing enterprises to achieve “innovation compensation” by reducing compliance costs and promoting
production eciency. For example, Bai (2019) argued that government R&D subsidies stimulated green innovations of energy-intensive rms. On the other
hand, the government endeavors to control and eliminate environmental pollution by means of environmental regulations, including source-oriented
treatments (Laplante and Rilstone, 2004)andend-of-pipe based treatments(Wang, 2019). For instance, Zhao et al. (2020) employed the GMM estimation
method to investigate the effects of environmental regulation on greenhouse gas emissions. They found that apart from the direct effect on CO2 emission
reduction, environmental regulations also indirectly restrain CO2 emissions by adjusting the structure of energy consumption.
However, it is dicult to reach a consensus about the inuence of driving factors on environmental pollution in the existing literature. For instance, some
researchers have revealed a positive relationship between population and energy-related pollution(Li et al., 2019; Liddle and Lung, 2010)while others have
reported that population impact on the environment is an inverted U-shaped curve(Zhang et al., 2018b). Similarly, some studies have argued that economic
growth tends to increase pollutant emissions(Dong et al., 2018), whereas substantial evidence also states that economic growth could arise from structural
transformation and advance production technology, and that these factors may thence offset the negative effects of growing economic activities on the
environment(Krueger, 1995). We summarize the divergent views of several representative factors and their impacts on pollutant emissions (See Table 1).
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It can be observed thatthe effect of factors on pollution emission are diverse and may even be mutually contradictory. The failure of the adopted symmetrical
analytical instruments to describe the practical asymmetric causal relationship may be a cause of the inconsistent ndings. This mismatch of analytical
instruments may lead to the attributes proved to be causally related in one situation to be unrelated or adversely related in another situation(Meyer et al.,
1993). In detail, the estimation of the marginal effect in quantitative methods may neglect some samples with weak signicance while valuing the samples
with large variances. For this reason, at the individual level, cases opposite to the observed net effects often appeared, that is, not every case in the sample
supported a xed relationship between the dependent and independent factors(Woodside, 2013).
In addition, few studies have investigated the interaction effects and combined impacts of multiple factors (especially three or more) on environmental
pollution at present. Far from being mutually exclusive, the pollution drivers not only coexist, but also prominently impact one another’s operation because of
causal complexity (a consequence of the mix of both the individual marginal effects and conjunction impacts induced by multiple causes). Traditional
quantitative approaches which aim to examine marginal effects - such as multiple regression analysis - may, to some extent, interpret the multiple
conjunctural causation between several variables. However, capturing interaction effects for in excess of three variables is arduous(Woodside, 2013).
Moreover, these methods are weak in their ability to both handle the causal complexity from a holistic level and uncover individual heterogeneity observed in
reality. For these reasons, this paper attempts to utilize the fsQCA method, which focuses on correlations between combinations of factors and the outcome,
also making explicit the impact of the context and the interaction effects between factors, to overcome these limitations.
Based on congurational theory, the QCA method is a set-theoretic approach by applying Boolean algebra to explore the combinations of organizational
attributes leading to the outcome at issue(Ragin, 2000). This method aims to combine approaches from quantitative and qualitative techniques; taking the
best attributes from both(Pappas and Woodside, 2021). Although there are other comparative evaluation approaches in the congurational theory such as
cluster analysis(Lim et al., 2006), deviation scores(Delery and Doty, 1996), and the interaction effects method(Dess et al., 1997), the QCA method is superior
in grasping causal complexity at a ne-grained degree and can enable scholars to glean statuses of equinality, substitution, and complementary effects
among variables(Greckhamer et al., 2018). In brief, QCA’s advantages in employing systematic comparisons over cases enable researchers to understand
complex interactions across multiple causal situations(Lobe, 2010).To date, QCA has witnessed extensive use in different elds such as business
strategies(Douglas et al., 2020), information systems(Park et al., 2020), and social networks(Rutten, 2020). However, it is less common in research upon
environmental pollution. The method will be suitable for the study of environmental governance considering that environmental problems are endowed with
the attribute of multi-factor causal complexity.
In conclusion, there are some deciencies in existent understanding. First, it is dicult to reach a consensus about the inuence of driving factors on
environmental pollution from a single factor perspective. Second, traditional quantitative analysis instruments, such as regression analysis, have advantages
in estimating the net effect of a single factor on the outcome ceteris paribus, whereas are dicult to elaborate multiple interaction effects (more than three
factors) considering the complicated statistical interpretations and the multicollinearity problems. Third, fsQCA method, which is suitable for handling the
causal complexity of multiple factors, is seldom used in environmental pollution research. To ll these gaps, this paper adopts fsQCA to investigate the casual
complexity of environmental pollution at the provincial level and provides environmental improvement paths for high-pollution provinces. This study
contributes to extant works in the following ways: 1) By introducing the fsQCA method creatively, this article assesses the multiple causation and asymmetric
causality of environmental pollution at the individual level, which lls the gaps of previous studies on inuencing mechanism of pollution factors. 2) We
compare the empirical results of econometric models and fsQCA method, and thus offer a ne-grained and comprehensive insight into the interactive
mechanism of environmental drivers; 3)This study depicts a strategic map for green development by considering the spatiotemporal characteristics and
environmental conguration changes, and provides the corresponding improvement paths for high-pollution provinces.
In the following section, we illustrate the specication of the fsQCA method. Then, we report the results and undertake a discussion of the implications of our
ndings for policymakers. The nal part of the paper presents the major conclusions.
2. Method And Data
There are three main variations: crisp-set QCA (csQCA), multi-value QCA (mvQCA), and fuzzy-set QCA (fsQCA). CsQCA is suitable to handle complex sets of
binary data(Ragin, 1987), while mvQCA, which regards variables as multivalued rather than dichotomous, is an extension of csQCA. Both csQCA and mvQCA
require their selected data to be classied according to explicit distinguish criteria; as a result, it is hard to grasp complexity in cases that naturally change by
level or degree(Rihoux and Ragin). FsQCA integrates fuzzy-sets and fuzzy-logic manners to break through this limitation; it offers a more ne-grained insight
into data by providing a more realistic approach in which variables can capture all values from 0 to 1. Therefore, fsQCA was applied to our research subject as
our variables had no clear classication criteria. The basic steps in fsQCA method are shown in Fig.1.
2.1. Selection of variables
The rst step in performing fsQCA analysis is specifying the congural model; identifying what antecedent conditions should be involved in estimation
accounting for the outcome(Douglas et al., 2020). On the basis of IPAT model (population, economy, and technology), two indispensable dimensions of
industry and government are extended in our research framework. Within these aspects, technological factors include technological innovation capacity and
R&D subsidy; governmental factors containing the environmental regulations on source treatment and end-of-pipe treatment; economic, demographic and
industrial factors include economic growth, population scale, and industry structure respectively. Within this framework, the conguration of seven
environmental driving factors that may lead to high or low environmental pollution is shown in Fig.2.
This paper used panel data from 30 provinces in China between 2011 and 2017. In view of the policy effects of the Five-Year Plan (2011-2015), we divided the
time spans into two sections (2011-2015 and 2016-2017) to provide a time-variant perspective. To avoid abnormal values in some provinces during a specic
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year, we calculated the average value of 2011-2015 and 2016-2017 respectively. In addition, the price took the deator coecient into account and 2011 was
considered as the base period (year 2011=100). The detailed data source of variables is presented in Table 2.
Specically, industrial pollution was adopted to represent environmental pollution, and includes, given available data, industrial wastewater and industrial
waste gas emissions (e.g., sulfur dioxide, smoke, dust, and nitrogen oxides). To circumvent subjective bias, the entropy method was used to calculate the
comprehensive index of environmental pollution as this method maximizes the overall situation of pollution(Jianqin et al., 2010).
The indicators representing technological innovation were R&D expenditure(Shen et al., 2021), the number of patents(Linares et al., 2019), the number of
researchers(Wen et al., 2020), full-time equivalents(Li et al., 2018), and new product sales(Bruno et al., 2006). Similarly, we used the entropy method to depict
the overall picture of technological innovation ability.
2.2. Data calibration
Following the stages outlined above, the original data should be calibrated to fuzzy-set membership scores (range from 0 to 1) that represent the membership
of a variable(Ragin, 2008). For example, 1 means the high level or presence of a dened set, whereas 0 means it is low level or absent. Based on Fiss (2011),
we used the rst quartile, the third quartile, and their average as the three qualitative anchors of fully out, fully in, and crossover point respectively. To these, we
then applied the direct calibration method in the fsQCA3.0 to transform the data into fuzzy-set memberships. Table 3 summarizes the descriptive statistics
and the calibration thresholds of the variables.
2.3. Necessity and suciency analyses
In the following, a truth table, which is a data matrix, should be constructed to provide all logically possible congurations of variables in 2k rows (k=number
of variables), where each row represents a specic conguration. Based on the memberships in the fuzzy sets, every case in our study is associated with a row
of the truth table. To identify whether a variable was necessary or sucient for an outcome, we then analyzed whether the conditions were always present (or
absent) in each case when the outcome was present. It follows, that if an innovative advantage is necessary to lead to low pollution in all regions, low
pollution will not happen if one region lacks such an advantage. Likewise, if an advantage in technological innovation is a sucient condition that leads to
low pollution, all regions with such an advantage will have low pollution.
The interpretive tools for both the necessary and sucient conditions are consistency and coverage. Consistency establishes the extent to which the cases
that share a conguration of variables agree in their outcome and is analogous to a correlation,while coverage displays the proportion of cases representing
an outcome in a certain conguration and is comparable with the coecient of determination (e.g., R2).
The consistency and coverage of conguration
is the membership of region
in the set of solution
and outcome, respectively.
To identify the necessary condition, the necessity analyses of all conditions and their negation were conducted with a consistency criterion of
0.9(Wagemann, 2012). Thereafter, suciency analyses were performed using the truth table algorithm to recognize congurations that were constantly
related to an outcome. To avoid “simultaneous subset” relations of congurations in both the outcome and its absence, the raw consistency benchmark of
suciency analysis must be more than 0.8 accompanied by a benchmark for PRI (proportional reduction in inconsistency) score of over 0.65 – the higher the
value, the more robust the solution(Misangyi and Acharya, 2014). In our study, we set the thresholds for raw consistency and PRI as 0.8 and 0.75, respectively.
Then we set the frequency threshold of one strong case for a conguration’s inclusion to ensure 100% of the studied sample in the suciency analysis (80%
is recommended proportion)(Greckhamer and Gur, 2021).
In the next step, and based on the truth table algorithm, the truth table rows should be logically simplied to classify causal conditions into core and peripheral
conditions. In this progress, there may be no practical cases of any particular conguration, which is a common problem named “limited diversity”. For this,
counterfactual analysis based on theoretical and substantive knowledge supports solving the limitations, and obtains parsimonious, intermediate, and
complex solutions(Wagemann, 2012). The core conditions appear in parsimonious solutions, while the peripheral conditions appear in the intermediate or
complex solutions. In general, core conditions are more convincing than peripheral conditions; the latter are relatively complementary. As a result, we placed
interpretative emphasis on parsimonious and complex solutions.
2.4. Robustness analyses
Wagemann (2012) proposes two set-theoretic-method-specic dimensions of robustness: the set-relational states of the dierent principles and the variations
in the coecients of t. Where dierent decisions bring about different solution terms but retain a subset relation between each other, the results can be
interpreted as robust. In like manner, where dierent decisions result in dierences in the coecients of t that are too insignicant to ensure a meaningfully
dierent substantive interpretation, the results can also be considered robust. Given this, the calibration threshold of the crossover point in this paper was
changed from the average value of the rst and third quartile to the median (Greckhamer and Gur, 2021). Thereafter, we recalculated the explained variable
(environmental pollution) by adding the production of solid waste. Next, we improved the threshold of raw consistency from 0.8 to 0.9, and the PRI from 0.75
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to 0.85. Finally, predictive validity was performed to test the accuracy of the models in the rst time window (2011-2015) using the data for the second time
window (2016-2017).
It should be noted that the rst robustness check with the alternative calibration may transform an empirically observed row into a logical remainder (or vice
versa), or from a consistent row into an inconsistent one (or vice versa), drawing different conclusions(Wagemann, 2012). However, minor changes are
observed in the results (including the specic number of solutions, solution consistency, solution coverage, and the characters of congurations), that is to
say, the explanations of the main conclusions remain substantively unchanged (see Supplementary Table S2-S3). As for the second and third robust tests, we
found that substitution of the explained variable and tighter threshold requirements produced substantially similar solutions with expected minor changes
including subset relations of congurations, solution consistency, and solution coverage (see Supplementary Table S4-S7). At last, the nding of the predictive
validity test shows that the congurations between 2011-2015 and 2016-2017 are highly consistent (see Table 6). In short, a series of robustness analyses
conrmed the credibility of the results presented in this paper.
3. Results
3.1. The regression analysis
To demonstrate the potential contribution of fsQCA in understanding environmental pollution compared to the quantitative analysis, we rst conducted the
traditional regression models, considering including all the same antecedent conditions and three technology-related interaction terms as independent
variables. As Table 4 shown, models 1-5 introduce different interaction terms when controlling individual xed effect and time xed effect. Several ndings are
summarized below.
First, for models 2-5, the coecients of technology-related interaction terms are signicant, and the signicance and value of interaction terms increase when
considering more interaction terms. The nding indicates that interaction effects between environmental drivers exist signicantly;
Second, by adding more interaction terms, the number of signicant parameters and the goodness of t (R2) increase from model 1 to model 5, which implies
that the inclusion of interaction terms enhance the explanatory power of models;
Third, the parameter signs of technology between model 2 and 5 are in opposite directions when three technology-related interaction terms are incorporated
into model 5, which illustrates that the impact of technology on environmental pollution is asymmetrical due to the existence of interaction effects;
Fourth, the parameters of other variables without corresponding interaction terms are insignicant. Combined with the conclusions above, it can be inferred
that the effect of a single factor is not signicant as that of a combination of multiple factors.
To sum up, the regression analysis conrmed the existence of interaction effects between pollution drivers. However, the construction of regression models
faces a dilemma. On the one hand, adding too many interaction terms into models will make results complicated and redundant. For example, the economic
implications of multiple interaction terms (especially for over three factors) will be dicult to interpret. More seriously, multiple interaction terms will result in
serious multicollinearity problems in the model, which then leads to a biased estimation; On the other hand, the absence of interaction terms will be
inconsistent with reality and lose partial explanatory power of the model.
Therefore, we then performed fsQCA analysis that can reveal the multiple interaction effects of environmental drivers while avoiding multicollinearity
We rstly identied the necessary condition of high pollution or low pollution. According to the results (see Supplementary Table S1), no variable strictly meets
the criteria of necessary conditions. This nding echoes the theory of complementarities that no organizational elements are best practices alone but will
affect positively only when they occur in conjunction with other elements.
3.1. Suciency analyses between 2011 and 2015
During the 12th Five-Year Plan Period (2011-2015), there were 10 congurations that led to either high pollution or low pollution in regions, as Table 5 presents.
These congurations illustrated that there were varied strategic paths that led to equinal outcomes, and this in turn, veries the existence of multiple causal
relationships in environmental issues. Further, these 10 pathways can be grouped into ve distinct pairs of neutral permutations (C1-C5). Pathways in each
pair represented the same core conditions and only varied in their complementary conditions.
The solution coverages of high pollution and low pollution were 0.587 and 0.755, respectively, which exhibits a strong explanatory power, whilst all the
congurations maintained very high consistencies (0.977 in high pollution, and 0.962 in low pollution); suggesting that these congurations are persuasive for
the outcomes.
3.1.1. Congurations for high pollution
There were ve congurations (C1a-C3) that illustrated the possible causal relationships that led to high pollution between 2011 and 2015. It is worth noting
that the rst four congurations (C1a-C2) contained the same core condition of possessing a backward industrial structure; this illustrates that structural
imbalance was the leading factor causing high pollution in the involved regions. Therefore, we label these four congurations as
structural imbalance
Specically, conguration 1a and 1b (C1a and C1b) featured technical lag, small R&D subsidies, large end-of-pipe treatment costs, small populations, and
backward industrial structures. These features signied that even though local government spent a large amount of money on end-of-pipe treatment,
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backward technological development and industrial structure were still harmful to the environment. In addition, the features of large source treatment costs
and backward industrial structure in C2 further revealed that the source treatment measure was ineffective for mitigating environmental burden when the
industrial structure was backward. In other words, no matter how much the government had spent on environmental regulation, a backward industrial
structure hindered environmental improvements to a greater extent.
In C3, the core conditions included both intensive cost on end-of-pipe treatment and large populations, with the peripheral conditions including advanced
innovation ability, strict environmental regulations, and the possession of highly developed economies. The representative regions of C3 are Guangdong and
Jiangsu, both are well-developed and densely populated. Specically, Guangdong and Jiangsu have topped China's provinces for the past decades in terms of
their recorded levels of GDP. These regions have coupled their possession of massive natural resources with rapid economic and social development. Apart
from this, the growth polar effect that has arisen as a consequence of economic agglomeration has undoubtedly attracted the inward migration of people
from surrounding regions. Rapid population growth and the possession of a large population will, therefore, not only lead to population agglomeration, but
also accelerate the consumption of limited resources, and bring about enormous population pressures. In turn, these effects stimulate further, economic-social
activities and may give rise to either predatory or disruptive use of resources. Increasing populations also give rise to huge levels of consumptive
pollution(Ehrlich and Holdren, 1971). Given these assorted facets, we labelled C3 as the
extensive population
3.1.2. Congurations for low pollution
According to Table 5, there were ve alternative congurations (C4a-C5b) that led to low pollution. C4a-C4c shared the same core conditions: advanced
industrial structure and low inputs for source treatment. In addition, they exhibited a comparatively ideal path for pollution governance in which local
government paid more attention to structural optimization than the cost of pollution treatments. Consequently, this conguration was labelled as
Specically, C4a featured smaller populations, advanced innovation abilities, substantial R&D support, advanced industrial structures, and low source
treatment costs. C4a included two municipalities, Beijing and Shanghai; these two cities have realized win-win situations that have balanced economic
development with environmental protection.
C4b and C4c were inferior in terms of technological innovations, governmental regulations, and economic development, but were superiorwith regard to their
industrial structures. The typical cases in this conguration included Yunnan and Heilongjiang, where tertiary industries make up more than half of the region’s
GDP. Yunnan, for example, records that its tourist industry accounted for 51.5% of its GDP in 2020. To respond to the national strategy “clear water and green
mountains are as valuable as mountains of gold and silver”, the local government attempted, in addition to continuing to promote the transformation and
upgrading of its tourism and cultural industry, to adopt a series of ecological measures, including developing green nance, implementing coal substitution,
and increasing forest carbon sinks; all of which are benecial to maintaining a low-pollution status.
C5a and C5b both possessed core conditions of low inputs for end-of-pipe treatment, less developed economies, and small populations with the peripheric
condition of low source treatment costs. These congurations indicated that these less developed areas could ensure low pollution whilst expending
(comparatively) less on environmental governance. One explanation for this is that a small population size alleviates the contradiction between humans and
nature; i.e. consumption-based pollution is reduced. Therefore, we labelled thesepathways as the
scarce population
3.2. Spatiotemporal variations of congurations
So that we could elaborate further on the evolutionary patterns of congurations, we further studied the sucient conditions between 2016 and 2017. As
evidenced in Table 6, we found seven congurations that led to high pollution whilst ve congurations resulted in low pollution. It can be observed that the
solution coverage and the solution consistency of these congurations were high; indicating a strong explanatory power for outcomes. The diverse pathways
indicated that multiple solutions existed for achieving the equinality of outcomes. In addition, several impressive ndings were obtained by comparing the
congurations in two time spans from temporal and spatial perspectives.
Path dependencies existed in regional development patterns. In the period between 2016 and 2017, the levels of the conditions (e.g., pollution levels,
environmental regulation inputs, and others) in most congurations were parallel with the former time span (2011-2015); indicating that there were strong
path dependencies in most provinces where few changes had been undertaken about their development patterns. For instance, for high polluted regions, the
states of seven conditions in C7b, C8a, and C8c were the same as C2, C1c, and C3, respectively. Similarly, for low polluted regions, C9a, C9b, C9c, and C10a
were similar to C4a, C4b, C4c, and C5a, separately. Therefore, we label these paths as being the same as for the previous period (see Table 6). To further
explore the extent of path dependency, predictive validity (using the second data set from 2016-2017 to compute the fuzzy scores for each of the ten
congurations in Table 5) was performed as presented in Table 7. Taking C1a for example, the second data set is largely consistent (98.2%) with the argument
that C1a is a subset of high pollution, and C1a accounts for 3.9% of the total memberships in high pollution. It is found that the consistency in most of the
congurations exceeds 0.95, which indicates that the congurations between 2011-2015 and 2016-2017 are highly consistent. Further, the high raw coverage
of each conguration, especially for C3 (0.360), C4b (0.304), C5a (0.469), conrms the strong path dependencies in regional development patterns.
There was a crowding-out effect in Shaanxi province. During the whole of the investigated period, Shaanxi spent hugely on R&D subsidies, but its
technological innovation capacity changed from high (C5b in 2011-2015) to low (C10b in 2016-2017). In other words, governmental R&D subsidies failed to
achieve the desired effect and instead eliminated regional technological innovation. This suggests that the crowding-out effect was more dominant in local
environmental governance. Specically, corporate R&D strategies stressed short-term benets while the subsidies offered by the local governments sought to
achieve long-term technical progress. Such a conict in the direction of R&D initiatives weakened the driving forces of R&D investments. It should also be
Page 7/17
noted that China currently faces challenges in supervising governmental R&D investments and that this may result in the misuse of R&D subsidies. This
regulatory defect can be seen to lead to inecient capital utilization.
To intuitively investigate the spatial distribution of conguration types and their transformations over time, this study applied the regional pollution labelling to
a map of China (see Fig.3). During the two periods, most regions in the involved types exhibited geographical adjacency. As Fig.3 illustrates, most of the
eastern regions belonged to the high-polluted group (e.g.,
extensive population
whilst western areas were mainly belonging to low-pollution clustering
green development
type). 
It may also be noted that the number of regions with
extensive population
technical laggard
types has grown and that most of them changed from the
structural imbalance
type. This transformation may be a consequence of continuous improvements to the quality and eciency of supply-side structural
reforms, a consequence of the constraints induced by the imbalanced structure being weakened, and technological and demographic factors becoming the
main drivers of environmental pollution. It can be noted, for instance, that the technical lag factor became the main driver that led to high environmental
pollution in northern regions while the possession of a large population base identied as the driving factor leading to severe pollution in central areas.
During the periods investigated, the large inputs of end-of-pipe treatments and the possession of backward industrial structures were common characteristics
shared by most high-polluted regions. In contrast, the low costs expended on source treatment and end-of-pipe treatment, the possession of smaller
populations, and less developed economies were common features for most low-pollution regions. To a great extent, it contributes to their unique
geographical advantages (e.g., climate,terrain, and vegetation resources) on the ecological environment and their original status of low pollution level (e.g.,
Yunnan). Further, local governments are confronted with multiple tasks from the central government, including economic growth and environmental
protection. Thereinto, environmental targets are obligatory in China’s performance evaluation system, but there are no incentives for local government to
surpass these targets(Zhang, 2020). In other words, the local governments in low-pollution areas just needed to input small environmental treatment costs to
surpass the cut-off score of environmental requirements decided by the central government.
4. Discussion And Implications
4.1. Implications from fsQCA
An extensive number of studies have explored the net effects induced by single factors on environmental issues from a symmetric perspective. These works
have tended to construct a linear or curvilinear relationship among theoretical elements of interest, and have failed to explore practical asymmetric causal
relationships. However, the fsQCA method enables heterogeneity to be revealed; something that traditional symmetric analytical methods challenge to
manage. The approach also results in a more ne-grained taxonomy of development types. This study explores what conditions of congurations are relevant
for pollution and how these conditions unite to work. The fsQCA method used in this paper provided new insights into these situations by developing a
multifaceted comprehension of the conditions’ dynamics. The results exposed subtle details of heterogeneity among the regions of China and identied sub-
groups for which various pathways lead to the same outcome. In addition, it was shown that the congurations of high pollution and low pollution disclosed
multiple causal relationships of variables, which demonstrates that the factors leading to environmental pollution at a provincial level are complex and
The causal complexity of environmental pollution in our ndings provides direct implications for local government when it comes to their addressing of
environmental issues. For example, whilst it is widely accepted that technological progress aids environmental protection(Pham et al., 2020), we found that,
for some regions (e.g., Henan, Hebei, and Anhui) of the
structural imbalance
type, their backward industrial structures still caused high pollution despite their
possession of high-level technological innovation capabilities. Therefore, the priority of local governments of the
structural imbalance
type is to optimize their
industrial structures.
This study also conrmed that the causal asymmetry derived from causal complexity may resolve a long-standing dispute about whether the relationship
between environmental pollution and its driving factor is positive or negative. For example, the effect of economic growth on environmental quality in
extensive population
type is negative while in
green development
type it is positive. This result is exactly because of the conjunction of the multiple conditions
(e.g., population, technology, economy, and governmental intervention), instead of being only (as advanced in previous studies) a consequence of economic
growth. Given this, our ndings can inspire future research to consider the causal complexity of environmental issues.
4.2. Path dependence
According to our results, regional environmental governance presented strong path dependence during the investigated period. As Fiss (2011) argues,
congurations and types appear to impact future congural states by inuencing the tracks of subsequent development models, thereby making certain
tracks more possible while decreasing the probability of others. In the present study, most regions showed strong path dependencies in their development
models; especially high-polluted regions. This leads to a signicant issue; namely, why do these regions become locked into development models that lack
dynamism, whilst other (rare) regions evade the lock-in effect and renovate themselves via consecutive new pathways. The lock-in effect works on a self-
reinforcing logic that prefers continuity and replication (David, 1985). Specically, the rst-mover advantage of one built-in development pattern decreases
operating costs through the scale effect, and the popularity of such patterns further leads to the improvement of the learning effect. Then, the synergetic effect
of both contributes to achieving a virtuous circle of self-reinforcing, thus maintaining the original pattern in a state of persistence or lock-in, unless with the
help of exogenous shock (e.g., policy reform). It follows, that the originally reasonable pattern and correcting errors in time are crucial to environmental
governance. The local government needs to consider the long-term impacts of policy implementation instead of just the short-term effects. In addition, the
government should take corrective actions as soon as possible once deviation between the practical effects of reform and its goal is detected.
Page 8/17
4.3. Crowding-out effect
In our ndings, the crowding-out effect that occurred in some regions, such as Shaanxi province, showed that government subsidies for technological
innovation reduced or eliminated the improvement of technological performances. This phenomenon may have occurred because government R&D contracts
were designed to bring social benets or long-run ecacy, whereas grantees such as private rms tend to pursue economic interests or improvements to short-
run performance. The divergence of original intentions between the two sides might crowd out the corporate R&D investment of the private and originally
planned research agenda. Another possible explanation from a rm's point of view is that R&D subsidies released possible liquidity constraint—as a cheaper
cost to apply for government subsidies than to raise funds in the capital market, enterprises considered the innovation subsidy an alternative source of
nancing instead of an actual R&D incentive. It follows that the government should lay greater stress on the improvement of the regulatory regime for rms
capital ows.
4.4. Policy implications for high-polluted regions
In our results, C9a (
green development
type) represented the best practice for achieving the dual targets of environmental protection and economic
development. Its core conditions included possessing an advanced industrial structure, high technological innovation capacity, and a relatively small
population. This is worth learning for high-polluted regions which were divided into
structural imbalance
extensive population
, and
technical laggard
Fig.4shows the advised paths to achieve a green environment for these high-polluted types.
Possession of a backward industrial structure was a common and signicant condition that led to high pollution in these polluted types. To address this, local
governments should rst optimize industrial structure. For instance, Beijing and Shanghai (
green development
type) which were low-pollution regions, began
their industrial transitions alongside declining dependence on pollution-extensive sectors. Their low-emission industries (e.g., telecommunication equipment
and transport equipment manufacturing) dominate their secondary industry sectors, and their tertiary industries have become pillars of their sustainable
economies. In direct contrast, the pillar industries of Shanxi (
structural imbalance
type), which is the high-polluted region, are predominantly concentrated in
high-emission elds, such as the smelting and pressing of ferrous metals, petroleum processing, and coke rening. Within such provinces, well-designed and
well-enforced industrial policies should be established to accelerate the independent elimination of pollution-intensive enterprises and the emergence of eco-
friendly industries; a process that will, in turn, minimize the negative by-product pollutants of economic development. In addition, inter-provincial cooperation
between the provinces should be encouraged.
For high-polluted regions of the
technical laggard
type (including Shanxi and Inner Mongolia), poor R&D subsidies and technological innovation abilities, and
high end-of-pipe treatment inputs indicated that their local governments tended to solve issues pertaining to current pollution rather than improving
technological innovation to ensure longer-term benets. To address this deciency and the problems associated with short-term localized vanity projects (and
the problems that they may create for successor administrations), the central government should establish a retroactive investigation mechanism for
environmental governance decisions. Former ocials who leave a legacy of environmental destruction should pay a penalty for their actions even if they have
left oce or retired. In addition, and especially when making major administrative decisions for environmental governance, local governments should be
required to be open and transparent, and to implement sound supervision and assessment mechanisms.
Local governments in these regions should also increase R&D subsidies and tax preferences for high-tech and low-emission enterprises. Concurrently, to
improve the utilization eciency of R&D subsidies, the government should perfect the supervision system to deter adverse selection problems. For example,
cooperative innovation organizations such as national engineering laboratories or industrial R&D centers are encouraged to be set up by enterprises,
universities, and research institutes. The government should also undertake external supervision of innovative organizations by designing a post-evaluation
system for R&D achievements.
Regarding the high-polluted regions with
extensive population
type, it is concluded that their large populations were the core factor that led to the huge levels
of their consumption-related pollution. Large population size will result in overconcentration of the population which may accelerate the superuous
consumption of natural resources, further stimulate economic-social activities, and even lead to predatory or disruptive use of resources. Finally, an increasing
population gives rise to huge consumptive pollution. Plus, the population concentrations and inows into the developed central megacities have caused
additional population pressures which have further enhanced resource waste. The government should complete the system for integrating urban and rural
development to optimize the spatial distribution of population among cities of different sizes. The alleviation of population pressure in large cities will assist
in the realization of a win-win situation between economic development, population density, and environmental protection.
5. Conclusion
This study creatively introduced a ne-grained analysis tool – the fsQCA method – to explore the interactive mechanism and causal complexity of
environmental drivers by using the panel data of 30 Chinese provinces in the period between 2011 and 2017. The ndings will provide targeted policy
recommendations for local governments at the provincial level. The main conclusions are as follows.
1) The regression analyses and necessity analyses conrm that no single factor is necessary condition for high or low pollution, and the interaction effects of
pollution drivers exist signicantly.
2) There are several different congurations of environmental drivers which lead to high pollution or low pollution in regions. This conrms the multiple
causality, asymmetry, and equinality of environmental issues;
3) The factors of industrial structure, population, and technological innovation are more signicant to achieving a state of ‘green environment’ compared to
environmental regulation factors;
Page 9/17
4) By testing predictive validity and analyzing the spatiotemporal variations of congurations in the two periods of 2011-2015 and 2016-2017, we found that
most regions showed strong path dependencies and spatial agglomeration for their development patterns.
Although this study lled some of the gaps within existing literature pertaining to the environmental area, the scope of this research is not broad enough due
to a lack of available data for some variables at a more ne-grained scale, such as city-level or prefecture-level. As the heterogeneity between cities is also
prominent, more new conclusions may be found in the future research at the city level.
The authors wish to express their sincere gratitude to the Fundamental Research Funds for the Central Universities (No. 2021CDJSKJC20 and
2021CDJSKCG28), the Natural Science Foundation of China (Grant No. 72071022 and 71801023), Chongqing Science & Technology Commission (No.
cstc2020jscx-msxmX0036) for funding this research project.
Data availability
The data used to support the ndings of this study are available from the corresponding author upon request.
Compliance with ethical standards
Conict of interest All the authors declare that they have no known competing nancial interests or personal relationships that could have appeared to
inuence the work reported in this paper.
Ethical approval Not applicable.
Consent to participate Not applicable.
Consent to publish Not applicable.
Authors contributionsYang Chen: Conceptualization, Methodology, Writing – original draft, Writing – review & editing. Jingke Hong: Conceptualization, Writing
– review & editing. Miaohan Tang: Data analysis, Resources. Yuxi Zheng: Visualization. Maoyue Qiu: Software. Danfei Ni: Writing – review & editing.
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Table 1. The relationships between pollutant emissions (e.g., CO2, SO2, NOx) and their several inuencing factors.
Inuencing factor Relationship Method Reference
Economic growth Positive Ridge regression model
Panel regression model
Dong et al. (2018); Wang et al. (2013)
Negative Time series analysis Yeh and Liao (2017)
Inverted U-shaped
curve GMM method
Instrumental variable method
Hanif and Gago-de-Santos (2017); Zhang et al. (2018b)
Population growth Positive Ridge regression
Panel regression model
Dong et al. (2018); Li et al. (2019); Liddle and Lung (2010);
Wang et al. (2013)
Inverted U-shaped
curve GMM method Hanif and Gago-de-Santos (2017); Zhang et al. (2018b)
Urbanization Positive Ridge regression
Spatial Durbin Panel model
Wang et al. (2013); Zhang (2018)
Negative Spatial econometric model Zhang and Xu (2017)
Inverted U-shaped
curve Vector error correction model Shahbaz et al. (2016)
progress Positive Panel regression model Wang and Wei (2020)
Negative Panel regression model Pham et al. (2020)
regulation Positive General equilibrium carbon
leakage model
Ritter and Schopf (2014)
Negative GMM method Zhao et al. (2020)
Page 12/17
Table 2.The basic prole of variables.
Factor Variable Explanation Reference
variable Environmental
pollution Calculated by the entropy method including the
following indicators:
Industrial wastewater
Industrial sulfur dioxide
Industrial smoke
Industrial dust
Industrial nitrogen oxides
Du et al. (2021); Huang et al. (2020); Jianqin et al. (2010)
Technology Technological
innovation (TI) Calculated by the entropy method including the
following indicators:
R&D expenditure
Number of patents
Number of researchers
Full-time equivalents
New product sales
Bruno et al. (2006); Li et al. (2018); Linares et al. (2019);
Shen et al. (2021); Wen et al. (2020)
R&D subsidy
(RS) Government subsidy on R&D Du et al. (2021)
Government Source
treatment (ST) The number of people in environmental protection
Laplante and Rilstone (2004)
treatment (ET) The investment in pollution abatement Wang (2019)
Economy Economic
growth (EG) GDP per capita Wang (2019)
Population Population
scale (PS) The number of people in the province Wang (2019)
Industry Industrial
structure (IS) The ratio of the added value of the tertiary industry
to that of the secondary industry
Du et al. (2021); Shen et al. (2021)
All data of selected variables come from
China Statistical Yearbook, China Statistical Yearbook on Environment,
China Statistical Yearbook on
Science and Technology.
Table 3. Descriptive statistics and calibration thresholds for the variables.
Page 13/17
Period Variable Mean SD Fully out, crossover, and fully in anchors for calibration
2011-2015 Pollution (Dimensionless) 28.6 22.0 13.0, 24.7, 36.4
TI (Dimensionless) 21.5 24.6 4.93, 13.7, 22.5
RS (Billion RMB) 6.94 10.3 2.19, 5.71, 9.22
ST (Thousand persons) 6.99 5.06 4.01, 6.85, 9.68
ET (Billion RMB) 23.5 16.2 15.0, 23.5, 31.0
EG (Thousand RMB per person) 40.4 17.5 29.1, 39.5, 49.9
PS (Million persons) 45.1 27.0 25.2, 42.5, 59.8
IS (%) 99.0 0.57 72.0, 86.0, 99.0
2016-2017 Pollution (Dimensionless) 26.4 22.0 11.1, 22.0, 33.0
TI (Dimensionless) 21.8 25.5 5.28, 14.7, 24.1
RS (Billion RMB) 7.45 10.7 2.78, 7.08, 11.4
ST (Thousand persons) 4.94 3.60 3.11, 4.62, 6.12
ET (Billion RMB) 21.0 14.7 10.6, 20.4, 30.2
EG (Thousand RMB per person) 40.1 17.9 28.3, 37.8, 47.2
PS (Million persons) 46.0 27.7 25.5, 43.5, 61.4
IS (%) 132 0.67 97.0, 113, 129
Notes: Obs2011-2015=150, Obs2016-2017=60; SD=Standard deviation.
Table 4. Congurations for high pollution and low pollution between 2011 and 2015.
2011-2015 High pollution Low pollution
C1a C1b C1c C2 C3 C4a C4b C4c C5a C5b
Raw coverage 0.072 0.074 0.104 0.174 0.326 0.203 0.388 0.362 0.491 0.070
coverage 0.038 0.050 0.040 0.108 0.256 0.174 0.040 0.012 0.135 0.035
Consistency 0.972 0.908 0.981 0.966 0.988 0.931 0.992 1.000 0.977 1.000
Typical regions
Shanxi Inner
Mongolia Anhui Henan,
Hebei Jiangsu,
Guangdong Beijing,
Shanghai Yunnan Heilongjiang Qinghai,
Jilin Shaanxi
Structural Imbalance Extensive
Population Green Development Scarce Population
coverage 0.587 0.755
consistency 0.977 0.962
Notes: Black circles indicate the high level (or presence) of a condition; circles with crosses indicate the low level (or absence) of a condition; large circles
indicate core conditions; small ones, peripheral conditions; and blank spaces indicate "don’t care".
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Table 5. Congurations for high pollution and low pollution between 2016 and 2017.
2016-2017 High pollution Low pollution
C6a C6b C7a C7b C8a C8b C8c C9a C9b C9c C10a C10b
coverage 0.085 0.076 0.072 0.156 0.111 0.139 0.360 0.191 0.304 0.296 0.328 0.066
coverage 0.046 0.040 0.034 0.087 0.031 0.086 0.259 0.159 0.035 0.028 0.229 0.036
Consistency 0.961 0.973 0.963 1.000 1.000 0.944 1.000 0.887 0.967 0.979 0.981 0.990
Shanxi Inner
Mongolia Jiangxi Henan,
Hebei Anhui Sichuan,
Liaoning Jiangsu,
Guangdong Beijing,
Shanghai Yunnan Heilongjiang Qinghai,
Jilin Shaanxi
Technical Laggard Extensive Population Green Development Scarce Population
coverage 0.716 0.763
consistency 0.979 0.953
Notes: Black circles indicate the high level (or presence) of a condition; circles with crosses indicate a low level (or absence); large circles indicate core
conditions; small ones, peripheral conditions; and blank spaces indicate "don’t care".
Table 6. Congurations for high pollution and low pollution between 2016 and 2017.
2016-2017 High pollution Low pollution
C6a C6b C7a C7b C8a C8b C8c C9a C9b C9c C10a C10b
coverage 0.085 0.076 0.072 0.156 0.111 0.139 0.360 0.191 0.304 0.296 0.328 0.066
coverage 0.046 0.040 0.034 0.087 0.031 0.086 0.259 0.159 0.035 0.028 0.229 0.036
Consistency 0.961 0.973 0.963 1.000 1.000 0.944 1.000 0.887 0.967 0.979 0.981 0.990
Shanxi Inner
Mongolia Jiangxi Henan,
Hebei Anhui Sichuan,
Liaoning Jiangsu,
Guangdong Beijing,
Shanghai Yunnan Heilongjiang Qinghai,
Jilin Shaanxi
Technical Laggard Extensive Population Green Development Scarce Population
coverage 0.716 0.763
consistency 0.979 0.953
Notes: Black circles indicate the high level (or presence) of a condition; circles with crosses indicate a low level (or absence); large circles indicate core
conditions; small ones, peripheral conditions; and blank spaces indicate “don’t care”.
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Table 7. The test for predictive validity.
Congurationfor high pollution Consistency Raw coverage
0.982 0.039
0.953 0.042
1.000 0.111
0.992 0.181
1.000 0.360
Congurationfor low pollution Consistency Raw coverage
0.887 0.191
0.967 0.304
0.966 0.271
0.985 0.469
0.990 0.066
; Negation (NOT),
; Logical conjunction (AND).
Figure 1
Basic steps in fsQCA method.
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Figure 2
Conguration analysis framework of environmental driving factors for leading to pollution.
Figure 3
The maps of congural types in the two time periods.
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Note: The abbreviations of regions detail in Appendix Table A2.
Figure 4
The advised paths to a green environment for three high-polluted types.
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