The Effects of Reverse Knowledge Spillover on China’s Sustainable Development: Sustainable Development Indicators Based on Institutional Quality
Abstract
:1. Introduction
2. Theoretical Background and Literature Review
2.1. Sustainable Development and Innovation
2.2. OFDI-Induced Reverse Knowledge Spillovers
3. Research Methods
4. Variable Specification
4.1. Sustainable Development Indicator
4.2. OFDI-Induced RKS
4.3. Innovation Performance
5. Empirical Findings
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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First level | Second Level | Third Level (j) | Units | Positive/Negative | |
---|---|---|---|---|---|
Composite Sustainable Development (SD) | Economic Development (SD_Economic) | 1 | Per capita gross regional product (GRP) | RMB yuan | Positive |
2 | The proportion of secondary industry and tertiary industry in the GRP | % | Positive | ||
3 | Total investment in fixed assets | Hundred million RMB yuan | Positive | ||
4 | Number of employees | 10,000 people | Positive | ||
5 | Per capita value of trade | US Dollars | Positive | ||
Social Development (SD_Social) | 6 | Per capita disposable income | RMB Yuan | Positive | |
7 | Per capita area of paved roads | Square meter | Positive | ||
8 | Per capita general public expenditure for education | RMB yuan | Positive | ||
9 | Per capita postal and telecommunications traffic | RMB yuan | Positive | ||
Environmental Development (SD_Environment) | 10 | Wastewater treatment rate | % | Positive | |
11 | Comprehensive utilization rate of industrial solid waste | % | Positive | ||
12 | Green covered area as % of completed area | % | Positive | ||
13 | Industrial waste gas emissions | Billion standard cubic meters | Negative | ||
14 | Total wastewater discharged | 10,000 t | Negative | ||
15 | Industrial solid wastes produced | 10,000 t | Negative |
Developed Economies | Emerging Economies |
---|---|
Netherlands; Sweden; United Kingdom; Singapore; United States; Germany; Korea, Rep; Japan; Chinese Hong Kong; France | Thailand; Russian Federation; Mongolia; South Africa; Brazil; India; Philippines; Malaysia; Kazakhstan; Ukraine |
Type | Variable | Proxy | Measurement (Units) | |
---|---|---|---|---|
Endogenous Variables | 1 | SD | Sustainable Development Indicator | Refer to Section 4.1 |
SD_Economic | Economic Development Indicator | |||
SD_Social | Social Development Indicator | |||
SD_Environment | Environment Development Indicator | |||
2 | INN | Innovation Performance | Number of patent grants/GRP (unit/RMB million yuan) | |
3 | RKS_ed | OFDI-Induced RKS from Developed Economies | RKS/GRP (%; refer to Section 4.2) | |
4 | RKS_ing | OFDI-Induced RKS from Emerging Economies | ||
5 | KS | FDI-Induced KS | KS/GRP (%; refer to Section 4.2) | |
Exogenous Variables | 5 | RD_exp | R&D Expenditure Intensity | R&D expenditure/GRP (%) |
6 | RD_per | R&D Personnel | Number of R&D personnel per million people (people) | |
7 | GRP | GRP | Per capital GRP (RMB yuan) | |
8 | Trade | Trade Intensity | Trade/GRP (%) | |
9 | FA_INV | Total Investment in Fixed Assets | Total investment in fixed assets/GRP (%) | |
10 | EMP | Employees | Number of employees (million people) | |
11 | IND | Industrial Structure | Output value of tertiary industry/output value of secondary industry (%) |
Variable | Mean | SD | Min | Max |
---|---|---|---|---|
SD | 0.310 | 0.158 | 0.08 | 0.83 |
SD_Economic | 0.242 | 0.189 | 0.04 | 0.79 |
SD_Social | 0.266 | 0.194 | 0.03 | 0.95 |
SD_Environment | 0.508 | 0.136 | 0.14 | 0.83 |
INN | 0.011 | 0.009 | 0.001 | 0.055 |
RKS_ed | 0.170 | 0.261 | 0 | 2.408 |
RKS_ing | 0.008 | 0.015 | 0 | 0.178 |
KS | 0.583 | 0.818 | 0.039 | 6.449 |
RD_exp | 0.014 | 0.011 | 0.002 | 0.074 |
RD_per | 3497 | 4025 | 289 | 21,273 |
GRP | 33,448 | 23,094 | 3603 | 118,198 |
Trade | 0.330 | 0.405 | 0.032 | 1.842 |
FA_INV | 0.645 | 0.227 | 0.240 | 1.371 |
EMP | 25.553 | 16.970 | 2.9 | 67.26 |
IND | 0.944 | 0.493 | 0.494 | 4.165 |
SD | SD_Economic | SD_Social | SD_Environment | ||||
---|---|---|---|---|---|---|---|
Equation (1): SD | |||||||
SD_lag | 0.582 *** | 0.831 *** | 0.425 *** | 0.588 *** | |||
(11.84) | (23.40) | (8.79) | (12.43) | ||||
INN | 0.944 *** | 0.182 | 1.818 *** | 0.676 | |||
(2.77) | (0.86) | (2.79) | (0.62) | ||||
RKS_ed | −0.072 ** | −0.016 | −0.114 * | −0.185 * | |||
(−2.29) | (−0.85) | (−1.83) | (−1.78) | ||||
RKS_ing | 1.021 * | −0.924 *** | 3.797 *** | 2.667 | |||
(1.66) | (−2.65) | (3.24) | (1.33) | ||||
KS | −0.010 ** | −0.007** | −0.003 | −0.024 | |||
(−1.99) | (−2.68) | (−0.36) | (−1.46) | ||||
_Cons | 0.131 *** | 0.057 *** | 0.126 *** | 0.227 *** | |||
(8.24) | (6.45) | (7.30) | (7.79) | ||||
Equation (2): INN | |||||||
INN_lag | 0.772 *** | 0.771 *** | 0.778 *** | 0.775 *** | |||
(25.63) | (26.11) | (25.93) | (26.15) | ||||
RKS_ed | 0.012 *** | 0.007 ** | 0.010 *** | 0.008 *** | |||
(3.73) | (1.99) | (2.64) | (2.72) | ||||
RKS_ing | −0.278 *** | −0.158 ** | −0.245 *** | −0.175 *** | |||
(−4.87) | (−2.36) | (−3.43) | (−3.17) | ||||
KS | −0.0004 | −0.001 | −0.001 | −0.001 | |||
(−0.86) | (−1.25) | (−1.32) | (−1.24) | ||||
RD_exp | 0.006 | 0.019 | 0.040 | 0.025 | |||
(0.08) | (0.25) | (0.51) | (0.35) | ||||
RD_per | 0.001 ** | 0.002 *** | 0.001 | 0.001 ** | |||
(2.01) | (2.65) | (1.38) | (2.26) | ||||
_Cons | −0.007 | −0.010 ** | −0.004 | −0.008 * | |||
(−1.48) | (−2.17) | (−0.88) | (−1.79) | ||||
Equation (3): RKS_ed | SD | Equation (4): RKS_ing | SD | Equation (5): KS | SD | ||
RKS_ed_lag | 0.139 *** | RKS_ing_lag | 0.0295 | KS_lag | 0.610 *** | ||
(3.30) | (0.71) | (17.01) | |||||
GRP | 0.052 | GRP | 0.001 | GRP | −0.079 | ||
(1.31) | (0.32) | (−1.19) | |||||
FA_INV | −0.188 ** | FA_INV | −0.008 | FA_INV | 0.069 | ||
(−1.97) | (−1.24) | (0.43) | |||||
Trade | −0.237 *** | Trade | −0.012 * | Trade | 0.151 | ||
(−2.63) | (−1.87) | (0.97) | |||||
EMP | 0.643 *** | EMP | 0.016 | EMP | −0.433 | ||
(3.30) | (1.24) | (−1.31) | |||||
IND | 0.152 *** | IND | 0.004 | IND | −0.072 | ||
(2.80) | (1.02) | (−0.81) | |||||
_Cons | −2.270 *** | _Cons | −0.045 | _Cons | 2.251 | ||
(−4.34) | (−1.28) | (2.37) |
SD | SD_Economic | SD_Social | SD_Environment | |
---|---|---|---|---|
The Effect of OFDI-Induced RKS from Developed Economies on Sustainable Development | ||||
Direct Effect | −0.072 | - | −0.114 | −0.185 |
Indirect Effect (through innovation) | 0.944 × 0.012 = 0.011 | - | 1.818 × 0.01 = 0.018 | - |
Total Effect | −0.061 | - | −0.096 | −0.185 |
The Effect of OFDI-Induced RKS from Emerging economies on Sustainable Development | ||||
Direct Effect | 1.021 | −0.924 | 3.797 | - |
Indirect Effect (through innovation) | 0.944 × −0.278 = −0.262 | - | 1.818 × −0.245 = −0.445 | - |
Total Effect | 0.759 | −0.924 | 3.352 | - |
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Dong, X.; Yu, C.; Hwang, Y.S. The Effects of Reverse Knowledge Spillover on China’s Sustainable Development: Sustainable Development Indicators Based on Institutional Quality. Sustainability 2021, 13, 1628. https://doi.org/10.3390/su13041628
Dong X, Yu C, Hwang YS. The Effects of Reverse Knowledge Spillover on China’s Sustainable Development: Sustainable Development Indicators Based on Institutional Quality. Sustainability. 2021; 13(4):1628. https://doi.org/10.3390/su13041628
Chicago/Turabian StyleDong, Xiaoxu, Cheon Yu, and Yun Seop Hwang. 2021. "The Effects of Reverse Knowledge Spillover on China’s Sustainable Development: Sustainable Development Indicators Based on Institutional Quality" Sustainability 13, no. 4: 1628. https://doi.org/10.3390/su13041628