3.1. The regression analysis
To demonstrate the potential contribution of fsQCA in understanding environmental pollution compared to the quantitative analysis, we first 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 fixed effect and time fixed effect. Several findings are summarized below.
First, for models 2-5, the coefficients of technology-related interaction terms are significant, and the significance and value of interaction terms increase when considering more interaction terms. The finding indicates that interaction effects between environmental drivers exist significantly;
Second, by adding more interaction terms, the number of significant parameters and the goodness of fit (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 insignificant. Combined with the conclusions above, it can be inferred that the effect of a single factor is not significant as that of a combination of multiple factors.
To sum up, the regression analysis confirmed 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 difficult 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 problems.
We firstly identified 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 finding 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. Sufficiency analyses between 2011 and 2015
During the 12th Five-Year Plan Period (2011-2015), there were 10 configurations that led to either high pollution or low pollution in regions, as Table 5 presents. These configurations illustrated that there were varied strategic paths that led to equifinal outcomes, and this in turn, verifies the existence of multiple causal relationships in environmental issues. Further, these 10 pathways can be grouped into five 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 configurations maintained very high consistencies (0.977 in high pollution, and 0.962 in low pollution); suggesting that these configurations are persuasive for the outcomes.
3.1.1. Configurations for high pollution
There were five configurations (C1a-C3) that illustrated the possible causal relationships that led to high pollution between 2011 and 2015. It is worth noting that the first four configurations (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 configurations as structural imbalance type.
Specifically, configuration 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 signified that even though local government spent a large amount of money on end-of-pipe treatment, 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. Specifically, 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 type.
3.1.2. Configurations for low pollution
According to Table 5, there were five alternative configurations (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 configuration was labelled as green development type.
Specifically, 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 configuration 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 finance, implementing coal substitution, and increasing forest carbon sinks; all of which are beneficial 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 configurations 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 type.
3.2. Spatiotemporal variations of configurations
So that we could elaborate further on the evolutionary patterns of configurations, we further studied the sufficient conditions between 2016 and 2017. As evidenced in Table 6, we found seven configurations that led to high pollution whilst five configurations resulted in low pollution. It can be observed that the solution coverage and the solution consistency of these configurations were high; indicating a strong explanatory power for outcomes. The diverse pathways indicated that multiple solutions existed for achieving the equifinality of outcomes. In addition, several impressive findings were obtained by comparing the configurations 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 configurations 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 configurations 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 configurations exceeds 0.95, which indicates that the configurations between 2011-2015 and 2016-2017 are highly consistent. Further, the high raw coverage of each configuration, especially for C3 (0.360), C4b (0.304), C5a (0.469), confirms 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. Specifically, corporate R&D strategies stressed short-term benefits while the subsidies offered by the local governments sought to achieve long-term technical progress. Such a conflict in the direction of R&D initiatives weakened the driving forces of R&D investments. It should also be 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 inefficient capital utilization.
To intuitively investigate the spatial distribution of configuration 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 type) whilst western areas were mainly belonging to low-pollution clustering (e.g., green development type).
It may also be noted that the number of regions with extensive population and 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 efficiency 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 identified 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.