2.1 fuzzy-set Qualitative Comparative Analysis
This study tries to analyze the land-based influencing conditions of MEE from the configuration perspective, which makes the fsQCA a suitable method for carrying out our empirical test. QCA was proposed by Ragin in the 1980s. By using QCA, researchers can find out the logical relationship (for instance: what kind of configuration formed by certain condition variables may lead to the emergence or disappearance of certain outcome?) between the matching patterns of different conditions and the outcomes through cross cases comparison, so as to further identify the synergistic effects of multiple condition variables on the premise of recognizing the causal complexity [14].
Comparing with quantitative research based on traditional regression analysis, the advantages of QCA are as follow [16]: (1) Through cross cases comparison, researchers can ensure the external promotion of empirical results to a certain extent on the basis of identifying the mechanism of condition variables. (2) Researchers can identify configurations with equivalent outcomes, which can help to understand the differential driving mechanism of outcomes in different case scenarios, and further discuss the matching relationship among condition variables. (3) Researchers can further compare the configurations that lead to the emergence or disappearance of outcomes, and broaden their theoretical interpretation dimension of specific research problems. Because under the logical premise of causal asymmetry, the condition variables that lead to the emergence of the outcome variables may be different from the condition variables that lead to the emergence of the "non-set" of the outcome variables.
QCA includes three basic categories: clear set qualitative comparative analysis (csQCA), fuzzy set qualitative comparative analysis (fsQCA) and multivalued set qualitative comparative analysis (mvQCA). csQCA and mvQCA are only suitable for dealing with category problems, while fsQCA can further deal with problems related to continuous changes and partial subordination [14][16]. Therefore, fsQCA has been widely used in empirical research in recent years.
2.2 TOE framework
TOE framework is mainly applied to the analysis of new technology adoption and its influencing factors, and a variety of analysis frameworks have been developed. Among existed analysis frameworks, the TOE framework proposed by Tornatzky and Fleischer in 1990 has the most extensive impact, which puts forward three factors affecting the adoption of organizational innovative technology [17–19]: Technology refers to the characteristics of various technical means and their relationship with the organization; Organization mainly includes organizational structure, organizational scale, system regulations, organizational resources, etc; Environment mainly refers to the industry structure, external pressure, institutional environment, etc. Recently, TOE framework is widely used in QCA analysis because it can help QCA users refine condition variables and construct configuration models from technology, organization and environment.
This study takes MEE as the outcome variable in the context of green technology application in coastal areas. The condition variables selected in this study include: (1) green technology innovation and green technology capability in the technology condition, (2) urbanization and environmental regulation intensity in the organization condition, and (3) peer competition pressure and openness in the environment condition. With the outcome variable and condition variables, we build a configuration model as shown in Fig. 1. The names and abbreviations the variables involved in the model are shown in Table 1.
Table 1
Measure
|
Name
|
Abbreviation
|
Outcome
|
Marine Eco-Efficiency
|
MEE
|
Conditions
|
Green Technology Innovation
|
GTI
|
Green Technology Capability
|
GTC
|
Urbanization
|
URB
|
Environmental Regulation Intensity
|
ERI
|
Peer Competitive Pressure
|
PCP
|
Openness
|
OPEN
|
Marine Eco-Efficiency. There is no unified definition of MEE in the present. Schaltegger and Sturm (1990) put forward the concept of ecological efficiency and defined it as the ratio of added value to increased environmental impact [20]. World Business Council for Sustainable Development (1995) defined ecological efficiency as meeting human needs and improving the quality of life by creating products and services with price competitive advantages, while controlling its environmental impact and resource utilization intensity within the earth's carrying capacity level [21]. The organization for economic cooperation and development (1998) extended this concept to governments, industrial enterprises and other organizations, and held that ecological efficiency refers to the efficiency of using ecological resources to meet human needs [22]. Although different institutions and scholars have made different definitions of ecological efficiency, they have reached a consensus on the basic connotation, that is, ecological efficiency is "creating more value with less impact" or "obtaining more benefits from less resources" [23–24]. Following this connotation, this study considers that MEE refers to the maximization of economic output and the minimization of environmental pollution with the least consumption of marine resources.
Technology Condition. Green Technology Innovation (GTI) and Green Technology Capability (GTC) are taken as two secondary conditions in the technology condition. (1) The sustainable configuration path of MEE is a concept related to the high-quality development of marine economy, and the driving force of high-quality economic development lies in technological innovation. Thus, from the perspective of sustainable development, to effectively improve MEE needs to rely on GTI [25–26]. (2) It is worth noting that possessing green technologies is different from the effective application of green technology. Technical capability is the ability to use technical knowledge effectively [27]. If technology innovation or technology progress is to have an effect on economic development, it must take the form of products [28]. From this point of view, if we want to make GTI work effectively on MEE, the GTC to transform GTI into products will be needed.
Organization Condition. Urbanization (URB) and Environmental Regulation Intensity (ERI) are taken as two secondary conditions in the organization condition. Most technology innovations occur and gather in cities [29]. (1) The advantages of cities in specialization and diversity, accumulation of human capital, formation of information exchange network and improvement of transaction efficiency are conducive to technological innovation [30]. Thus, cities have technological advantages and scale effects in improving resource utilization and pollution control. In this case, the higher the URB of coastal areas, the more likely it is to carry out green technology innovation and have higher green technology capacity. (2) Unexpected output is an important part of MEE accounting, which refers to marine environmental pollution. Due to the externality of marine environmental pollution, it is difficult to effectively solve this problem by relying solely on market mechanism, so that environment regulation has become an important means to make up for market failure and solve environmental problems [31]. Environment regulation not only increases the production cost of enterprises, but also forces enterprises to carry out green technology innovation [32–33]. Therefore, the ERI not only reflects the attention of governments in coastal area to the marine environment, but also is closely related to the innovation and application of green technology, which has an important impact on MEE.
Environment Condition. Peer Competition Pressure (PCP) and Openness (OPEN) are taken as two secondary conditions in the environment condition. (1) From the perspective of intergovernmental relations, the competitive pressure between governments at the same level constitutes an important environment condition [34]. Under the institutional background of China, the central government personnel appointment system based on the relative performance appraisal of officials profoundly shapes the behavioral logic of local governments [35]. When facing the pressure brought by superior task (such as the construction of marine ecological civilization), there will be competition between governments at the same level, and the winner will receive additional incentives. Thus, PCP drives the local government to improve the performance on the corresponding task [36]. (2) The openness of a region affects its MEE. Openness may bring technology diffusion effect, promote the flow and matching of green innovation elements, and promote product and technology innovation [37]. However, Openness may also lead to pollution transfer and the emergence of a "pollution paradise" [38].
2.3 Variables and Data Source
2.3.1 Research Area
In this study, 11 coastal areas were selected as research units, including Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Guangxi and Hainan. According to the geographical location of each area, Tianjin, Hebei, Liaoning and Shandong are located around the Bohai Sea; Shanghai, Jiangsu and Zhejiang are located in the Yangtze River Delta; Fujian, Guangdong, Guangxi and Hainan are located in the Pan Pearl River Delta. The seas adjacent to the 11 areas are the Bohai Sea, the Yellow Sea, the East China sea and the South China Sea. The correlation between the marine system and the land system is increasing, which makes land-based pollution the main source of marine environmental pollution [39]. Since it is difficult to directly quantify the exhaust gas, non-aqueous and solid waste discharged from land, the existing literature often selects the municipal or provincial administrative region when selecting the research regional unit, because the statistical yearbook of relevant data generally takes the municipal or provincial regional unit as the statistical object [6–12]. Considering that the distinction between different sea areas may not be significant when taking municipal administrative regions as the research unit, 11 coastal provincial administrative regions are selected as the research regional units.
2.3.2 Outcome Variable
The outcome variable of this study is MEE. Our data source for measuring PCP is the annual evaluation of ecological civilization construction jointly initiated by National Bureau of Statistics, the National Development and Reform Commission, the Ministry of Ecology and Environment and the Organization Department of the Central Committee. The evaluation is conducted once every five years, and the most recent data is the “Bulletin of Results of 2016 Annual Evaluation of Ecological Civilization Construction”. Considering the lag of the impact of land-based conditions on MEE, this study selects the data of the year 2017 to calculate MEE, while the six condition variables are measured with the data of the year 2016.
Referring to the existing research literature [8–9], considering the availability, comparability and operability of data, this study constructs the MEE evaluation index system as shown in Table 2.
Table 2
Measurement indices and Data Source of Marine Eco-Efficiency
Outcome
for fsQCA
|
Indicator Type
|
First-Level Indicators
|
Secondary Indicators
|
Data Source
|
MEE
|
Input
|
Capital
|
Marine Economic Capital
|
Statistical Yearbook of China's Marine Economy
|
Labor
|
Marine-related Employment
|
Statistical Yearbook of China's Marine Economy
|
Energy
|
Electricity Consumption
|
China Statistical Yearbook
|
Environment Protection
|
Completed investment in industrial pollution control
|
China Statistical Yearbook
|
expected output
|
Economic Benefit
|
Gross Ocean Product (GOP)
|
Statistical Yearbook of China's Marine Economy
|
Ecological Benefit
|
Proportion of class I and class II seawater quality
|
Bulletin on Environmental Quality of China's Coastal Waters
|
unexpected output
|
Environment Pollution
|
Waste Gas Emission
|
China Statistical Yearbook
|
Wastewater Discharge
|
China Statistical Yearbook
|
Solid waste Discharge
|
China Statistical Yearbook
|
Input indicators include capital, labor, energy and environmental protection inputs. Marine economic capital is selected as the quantitative index of capital investment, marine-related employment as the quantitative index of labor investment, electricity consumption as the quantitative index of energy investment, and industrial pollution control investment as the quantitative index of environmental protection investment.
Expected output indicators include economic benefit and ecological benefit. The GOP is selected as the quantitative index of economic benefit, and the proportion of class I and class II seawater quality is selected as the quantitative index of ecological benefit.
The unexpected output indicator is environment pollution. Waste gas emission, waste water discharge and solid waste discharge are selected as the quantitative indicators of environment pollution.
Except for the data of GOP and seawater quality that are directly related to the ocean and its ecological environment, other data do not clearly distinguish whether they are derived from land or sea. Based on the practice of Ding et al. [9], these data are corrected by using the ratio of GOP / GDP:
marine economic capital = fixed asset investment in coastal areas * GOP / GDP
marine-related employment = employment in coastal areas * GOP / GDP
electricity consumption = electricity consumption in coastal areas * GOP / GDP
completed investment in industrial pollution control = completed investment in industrial pollution control in coastal areas * GOP / GDP
waste gas (wastewater and solid waste) emission = Waste gas (wastewater and solid waste) emission in coastal areas * GOP / GDP
Referring to the methods of dealing with unexpected output in the existing literature, this study uses super SBM-DEA model to calculate MEE, and uses MaxDEA Ultra 8 to process the data. The accounting results are shown in Table 3 and Fig. 2.
Table 3
Accounting results of MEE as Outcome Variable
DMU
|
Marine Eco-Efficiency
|
Tianjin
|
1.071
|
Hebei
|
0.454
|
Liaoning
|
1.191
|
Shanghai
|
1.098
|
Jiangsu
|
0.683
|
Zhejiang
|
0.397
|
Fujian
|
0.611
|
Shandong
|
0.331
|
Guangdong
|
1.046
|
Guangxi
|
1.186
|
Hainan
|
1.211
|
2.3.3 Condition Variables
The quantitative methods and data sources of conditional variables are shown in Table 4.
Table 4
Measurement indices and Data Source of Conditions Variables
Conditions
for fsQCA
|
Indicators
|
Data Source
|
GTI
|
(number of invention patents authorized + number of utility patents authorized) /10,000 persons
|
China Statistical Yearbook
|
GTC
|
Location Entropy
|
China Statistical Yearbook on High Technology Industry
|
URB
|
urban population / total population at the end of the year
|
China Statistical Yearbook
|
ERI
|
completed investment in industrial pollution control / 10,000 persons
|
China Statistical Yearbook
|
PCP
|
ranking of green development index
|
Bulletin of 2016 annual evaluation results of ecological civilization construction
|
OPEN
|
total foreign investment by region / 10,000 persons
|
China Statistical Yearbook
|
GTI. Based on the existing research [40], this study uses "(number of invention patents authorized + number of utility patents authorized) / 10,000 persons" to measure GTI. The larger the value is, the higher level of GTI will be.
GTC. The impact of GTI on MEE needs to have the GTC to transform GTI into products, and the degree of high-tech industry agglomeration is the direct embodiment of GTC. The core of high-tech industry is scientific and technological innovation [41]. High-tech industry can improve the utilization rate of resources and energy through technology innovation, thus, it has the characteristics of environment-friendly and resource-saving green industry [42]. As a strategic industry of economic development, high-tech industry has become an important driving force for the country to achieve green development. According to the classification in China Statistical Yearbook on High Technology Industry, high-tech industries mainly include six industries: (1) information chemical manufacturing, (2) medical instrument and equipment manufacturing, (3) computer and office equipment manufacturing, (4) electronic and communication equipment manufacturing, (5) aviation, spacecraft and equipment manufacturing, and (6) pharmaceutical manufacturing. The degree of high-tech industrial agglomeration can be calculated by using the method of Location Entropy [43]. If the location entropy is greater than 1, the high-tech industry agglomeration in this region has advantages in the whole country; Otherwise, the region is at a disadvantage. The calculation formula of location entropy is as follow:
GTC = HT ij = (qij / Σqj) / (qi / Σq)
This study classifies the above six industries into high-tech industries i. HTij is the national location entropy of industry i in region j, qij is the output value of industry i in region j, and Qj is the output value of all industries in region j. Qi refers to the output value of industry i nationwide, and Q is the output value of all industries nationwide.
URB. This study uses the proportion of urban population in the total population at the end of the year to measure URB of each coastal area.
ERI. This study uses the completed investment of industrial pollution control / 10,000 persons to measure ERI.
PCP. In 2016, the annual evaluation of ecological civilization construction was carried out nationwide in accordance with the requirements of The Evaluation And Assessment Measures For Ecological Civilization Construction Objectives issued by the General Office of the CPC Central Committee and the General Office of the State Council, The Green Development Index System and The Evaluation Objective System For Ecological Civilization Construction issued by the National Development and Reform Commission, the National Bureau of Statistics, the National Development and Reform Commission, the Ministry of Ecology and Environment and the Organization Department of the Central Committee. At the end of 2017, the Bulletin Of 2016 Annual Evaluation Results Of Ecological Civilization Construction was issued. We use the ranking of green development index of coastal areas as the measurement index of PCP. The bigger the ranking number of the area is, the higher PCP the area is facing.
OPEN. This study uses the total foreign investment /10,000 persons in different regions to measure OPEN of each coastal area.
According to the measurement method adopted in this study, the data processing results of each conditional variable are shown in Table 5.
Table 5
Data Processing Results of Conditions Variables
Area
|
GTI
|
GTC
|
URB
|
ERI
|
PCP
|
OPEN
|
Tianjin
|
23.195
|
1.095
|
0.829
|
66.323
|
28
|
4.488
|
Hebei
|
3.214
|
0.292
|
0.533
|
33.262
|
20
|
0.669
|
Liaoning
|
5.081
|
0.499
|
0.674
|
44.279
|
27
|
1.839
|
Shanghai
|
22.391
|
1.54
|
0.879
|
214.664
|
4
|
7.454
|
Jiangsu
|
19.85
|
1.478
|
0.677
|
93.485
|
9
|
4.869
|
Zhejiang
|
26.891
|
0.6776
|
0.67
|
107.669
|
3
|
2.825
|
Fujian
|
12.721
|
0.791
|
0.636
|
58.407
|
2
|
2.984
|
Shandong
|
8.593
|
0.613
|
0.59
|
127.08
|
18
|
1.31
|
Guangdong
|
14.254
|
2.204
|
0.692
|
24.076
|
13
|
3.67
|
Guangxi
|
2.417
|
0.704
|
0.481
|
26.96
|
12
|
0.611
|
Hainan
|
1.695
|
0.734
|
0.568
|
17.599
|
6
|
0.785
|
2.4 Data Calibration
In fsQCA method, calibration refers to the process of assigning collective membership to cases [44]. Specifically, researchers need to calibrate variables into sets according to existing theoretical knowledge and case scenarios. The membership degree of the calibrated set will be 0 ~ 1, and the calibration points include “fully in”, “crossover” and “fully out”. Due to the lack of clear theoretical concepts and external standards as the calibration basis for MEE and land-based conditions and, this study calibrates based on case descriptive statistics [14]. Referring to the existing research [45], calibration points in this study are set as the upper quartile, the lower quartile and the mean value of sum of the upper and lower quartiles of the sample data respectively. The calibration points of each variable are shown in Table 6.
Table 6
Calibration for Outcome and Conditions
Outcome and Conditions
|
Calibration points
|
Fully in
|
Crossover
|
Fully out
|
MEE
|
1.142
|
0.837
|
0.533
|
GTI
|
21.121
|
12.634
|
4.418
|
GTC
|
1.287
|
0.966
|
0.645
|
URB
|
0.685
|
0.632
|
0.579
|
ERI
|
100.577
|
65.344
|
30.111
|
PCP
|
19
|
12
|
5
|
OPEN
|
0.906
|
0.601
|
0.297
|
This study uses fsQCA3.0 to analyze the data. Since the crossover value of PCP in Guangxi is exactly 0.5 after calibration, according to the "partial subordination" of this crossover value, we adjust 0.5 to 0.501 [45]. Table 7 is the truth table or each variable after calibration.
Table 7
Area
|
MEE
|
GTI
|
GTC
|
URB
|
ERI
|
PCP
|
OPEN
|
Tianjin
|
0.91
|
0.98
|
0.77
|
1
|
0.52
|
1
|
1
|
Hebei
|
0.02
|
0.03
|
0
|
0
|
0.06
|
0.97
|
0.01
|
Liaoning
|
0.97
|
0.06
|
0.01
|
0.92
|
0.14
|
1
|
0.25
|
Shanghai
|
0.93
|
0.97
|
1
|
1
|
1
|
0.03
|
1
|
Jiangsu
|
0.18
|
0.93
|
0.99
|
0.93
|
0.92
|
0.22
|
0.99
|
Zhejiang
|
0.01
|
0.99
|
0.06
|
0.9
|
0.97
|
0.02
|
0.43
|
Fujian
|
0.1
|
0.51
|
0.16
|
0.56
|
0.36
|
0.01
|
0.46
|
Shandong
|
0.01
|
0.19
|
0.04
|
0.08
|
0.99
|
0.93
|
0.03
|
Guangdong
|
0.89
|
0.64
|
1
|
0.97
|
0.03
|
0.61
|
0.75
|
Guangxi
|
0.97
|
0.03
|
0.08
|
0
|
0.04
|
0.501
|
0.01
|
Hainan
|
0.98
|
0.02
|
0.1
|
0.03
|
0.02
|
0.07
|
0.07
|