Digital Economy and Environmental Sustainability: Do Information Communication and Technology (ICT) and Economic Complexity Matter?
Abstract
:1. Introduction
2. Review of the Literature
Hypothesis Formulation
3. Materials and Methods
3.1. Econometric Method and Process
3.2. Panel Cointegration Approaches
4. Results and Discussion
4.1. Summary Statistic
4.2. Cross-Sectional Dependence Tests
4.3. Panel Unit Root Tests
4.4. Panel Cointegration Tests
4.5. FM-OLS Panel Model
5. Conclusions
- I.
- ICT export and ICT import positively and negatively influence environmental sustainability in terms of ecological footprint, respectively. This empirical finding implies that importing highly advanced ICT infrastructure can help to decrease environmental unsustainability in the investigated region. Meanwhile, the production and export of these (ICT infrastructure) might be paid as high environmental cost. This evidence suggests that the governments and policymakers of these regions should implement policy to encourage the penetration of the ICT sector to maintain environmental sustainability.
- II.
- The significance of ECI in boosting the economy has been widely considered and accepted in this role. However, current debates on controlling effects of ECI to environmental aspects also draw enormous attention from a wide range of policymakers and concerned authorities. By the same token, the empirical results for economic complexity positively affect EcoFP. This infers that prevailing economic transformation (to knowledge- and skill-based) of industrial structure and economic activities in selected G-seven economies exploits environmental sustainability and is not environmentally friendly. Therefore, the concerned authorities should consider economic activities and complex structure of industries while implementing environmental sustainability policies.
- III.
- Likewise, the empirical evidence provides that FDI and trade activities will not help G-seven economies to reduce environmental unsustainability by lowering ecological footprint. Foreign investors and trade are not helpful to bringing environmentally friendly technology to the host countries to reduce environmental unsustainability. Therefore, the governments of the investigated region should encourage foreign investors and domestic traders to bring greener technology, which will not only help the G-seven countries in environmental terms, but also in economic condition.
- IV.
- Regarding research and development, it also negatively affects environmental unsustainability by reducing ecological footprint. The governments of the investigated region should invest more in research and development to reduce the harmful effect of economic growth on the environment. The research and development can also be helpful in structure transformation of the economy toward green economy.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Authors | Regions | Time Periods | Methodology | Results |
---|---|---|---|---|
Relationship between ICT and environmental unsustainability | ||||
[13] | Caribbean and Latin American economies | 1995 to 2017 | Continuously upgraded and fully modified (CUP-FM) and Biased corrected (CUP-BC) models | The results obtained from the models suggest that ICT increases environmental sustainability by reducing carbon emission. |
[12] | Six ASEAN economies | 1995 to 2017 | Generalized method of moment (GMM), CUP-FM and CUP-BC models | ICT decreases the intensity of carbon emissions in ASEAN economies |
[40] | Tunisian economy | 1975 to 2014 | Autoregressive Distributed Lag (ARDL) | The suggested empirical evidence indicates statistically insignificant relationship between ICT and carbon emission in investigated region. |
[74] | Forty-four Sub-Sharan African economies | 2000 to 2012 | GMM | There is negative relationship between ICT and carbon emissions in targeted region. |
[20] | Twenty-one Sub-Saharan African economies | 1996 to 2014 | Panel Corrected Standard Error (PSCE) and Feasible Generalized Leas Square (FGLS) | ICT, energy consumptions, and carbon emissions have positive relationship in Sub-Saharan African countries |
[15] | Chinese provinces | 2001 to 2016 | Panel quantile regression model (PQR) | All measures of ICT support to decrease carbon emissions in Chinese economy |
[17] | European Union (EU) | 2001 to 2014 | ARDL-Pooled Mean Group (ARDL-PMG) | The use of internet (proxy for ICT) declines environmental sustainability. The findings also suggest that EU economies did not achieve the goal of green economy. |
[19] | Iran | 2002 to 2013 | Dynamic OLS | The findings suggest different results, such as ICT in services and transportation sector have negative effect on environmental degradation while, in the industrial sector, ICT and carbon emissions have positive relationship |
[22] | BRICS countries | 1990 to 2015 | CUP-BC and CUP-FM | The development of ICT significantly reduces carbon emissions in investigated region |
[61] | Nine Asian countries | 1990 to 2018 | ARDL | The emergence of ICT in economy significantly influences carbon emissions in all countries. |
Relationship between ECI and environmental unsustainability | ||||
[75] | Portugal, Ireland, Italy, Greece, and Spain (PIIGS) economies | 1990 to 2019 | Dynamic OLS | The empirical findings suggest the nonlinear relationship between ECI and carbon emission. This relationship supports EKC hypothesis and N-shaped relationships in the long run. |
[76] | France | 1964 to 2014 | Dynamic OLS | The results show that ECI overturns the intensity of carbon emissions in France. The empirical evidence also supports the existence of U-shaped association between variables |
[57] | One hundred and eighteen developed and developing economies | 2002 to 2014 | System-GMM | ECI has significantly positive effect on carbon emissions in investigated region |
[56] | Twenty-eight OECD economies | 1990 to 2014 | Augmented Mean Group (AMG), Common correlated AMG (CCEMG), ARDL, Dynamic OLS, and FM-OLS | ECI reduces environmental unsustainability and can help to mitigate environmental effects |
[5] | G-seven economies | 1996 to 2019 | Fully modified ordinary least square (FM-OLS) and Dynamic OLS (DOLS) | The linear impact of ECI declines environmental sustainability, while the nonlinear (ECI2) supports the presence of EKC hypothesis |
[24] | EU | 1995 to 2016 | FM-OLS and DOLS | ECI significantly affects greenhouses green emissions in EU economies for the long run. |
[9] | United States of America | 1965 (Quarter-1) to 2017 (Quarter-4) | Quantile autoregression distributed lag (QARDL) model | ECI significantly increases EcoFP in USA. |
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Variables | Abbreviations | Measures | Nature | Source |
---|---|---|---|---|
Ecological footprint | EcoFP | Per capita ecological footprint (in global hectares) | Main | Global Footprint Network (GFN) |
Informational communication and technology (export) | ICTexp | Export of ICT-related goods | Main | World Development Indicators (WDI) https://databank.worldbank.org/ (accessed on 23 February 2022). |
Informational communication and technology (import) | ICTimp | Import of ICT-related goods | Main | WDI |
Economic Complexity (index) | ECI | The production composition face of a country by installing and including the information of their variety of range (the number of exported product) | Main | ALTAS of Economic Complexity https://atlas.cid.harvard.edu/rankings (accessed on 23 February 2022). |
Economic growth | GDPpc | Gross domestic products per person (measure in current price of USD) | Main | WDI |
Foreign Direct Investment | FDI | Net inflow of FDI (percentage of GDP) | Control | WDI |
Research and Development | RD | Expenditure on research and development (percentage of GDP) | Control | WDI |
Trade Ratio | TRA | Trade ratio in percentage of GDP | Control | WDI |
Population | POP | People living per square kilometer of land area (population density) | Control | WDI |
Variables | lnEcoFP | lnICTexp | lnICTimp | lnGDPpc | lnECI | lnFDI | lnPOP | lnTRA | lnRD |
---|---|---|---|---|---|---|---|---|---|
Mean | 1.777 | 1.713 | 2.207 | 10.580 | 0.439 | 0.296 | 4.541 | 3.892 | 0.739 |
Max | 2.324 | 3.013 | 2.749 | 11.052 | 1.022 | 2.478 | 5.861 | 4.482 | 1.223 |
Min | 1.430 | 0.512 | 1.569 | 9.927 | −0.616 | −6.393 | 1.241 | 2.973 | 0.039 |
S.D. | 0.261 | 0.671 | 0.318 | 0.213 | 0.390 | 1.236 | 1.500 | 0.393 | 0.325 |
Skew | 0.691 | 0.100 | −0.093 | −0.726 | −1.089 | −2.130 | −1.274 | −0.689 | −0.422 |
Kurt | 2.109 | 2.111 | 2.032 | 3.693 | 3.814 | 10.796 | 3.240 | 2.338 | 2.198 |
JB | 14.20 | 4.353 | 5.100 | 13.600 | 28.419 | 397.97 | 34.39 | 12.292 | 7.061 |
Prob | 0.000 | 0.113 | 0.078 | 0.001 | 0.000 | 0.000 | 0.000 | 0.002 | 0.029 |
Variables | Pesaran CD | B-P LM | H0 |
---|---|---|---|
lnEcoFP | 15.41 *** | 242.94 *** | Rejected |
lnICTexp | 18.92 *** | 325.50 *** | Rejected |
lnICTimp | 14.05 *** | 205.97 *** | Rejected |
lnGDPpc | 14.88 *** | 233.47 *** | Rejected |
lnECI | 12.44 *** | 177.70 *** | Rejected |
lnFDI | 2.20 ** | 28.49 | Rejected at CD |
lnPOP | 4.85 *** | 208.22 *** | Rejected |
lnTRA | 8.08 *** | 209.89 *** | Rejected |
lnRD | 5.09 *** | 202.06 *** | Rejected |
Variable | Level | First Difference | Order | ||
---|---|---|---|---|---|
No Trend | with Trend | No Trend | with Trend | ||
IPS (2003) | |||||
lnEcoFP | 2.8165 | −0.4742 | −4.0519 *** | −3.0111 *** | 1st difference |
lnICTexp | 0.4289 | 2.1736 | −3.2769 *** | −2.9242 *** | 1st difference |
lnICTimp | −1.1060 | −0.7257 | −5.7259 *** | −4.4426 *** | 1st difference |
lnGDPpc | −3.9456 *** | −1.8660 ** | - | - | Level |
lnECI | −1.3804 * | 1.1144 | −4.2139 *** | −3.5384 *** | 1st difference |
lnFDI | −2.4363 *** | −2.3832 *** | - | - | Level |
lnPOP | 0.0863 | 2.5328 | 0.1925 | −1.3963 ** | 1st difference |
lnTRA | −0.6490 | −0.9442 | −4.8942 | −3.8293 | 1st difference |
lnINF | −2.3324 | −1.7257 | −3.0346 *** | −2.8874 *** | 1st difference |
lnRD | 2.1801 | −1.4298 * | −3.2647 *** | −1.4815 ** | 1st difference |
Cross-sectional IPS (CIPS) | |||||
lnEcoFP | −2.361 ** | −2.129 | - | - | Level |
lnICTexp | −1.934 | −2.254 | −3.886 *** | −3.950 *** | 1st difference |
lnICTimp | −2.365 ** | −2.744 | - | - | Level |
lnGDPpc | −1.399 | −1.998 | −3.101 *** | −3.028 *** | 1st difference |
lnECI | −2.721 *** | −4.269 | - | - | Level |
lnFDI | −3.232 *** | −3.292 ** | - | - | Level |
lnPOP | −0.867 | −0.987 | −3.077** | −1.357 | 1st difference |
lnTRA | −1.248 | −1.612 | −2.483 ** | −2.942 *** | 1st difference |
lnRD | −2.160 | −2.359 | −4.133 *** | −4.141 *** | 1st difference |
Pedroni | Kao | |||
---|---|---|---|---|
Dimensions | ADF | |||
Statistics | Within-Dim | Between-Dim | t-Statistics | Prob. |
Panel v-stat | 1.691 ** | - | −3.7063 *** | 0.0001 |
Panel PP-stat | −2.616 *** | - | - | - |
Panel ADF-stat | −2.758 *** | - | - | - |
Group PP-stat | - | −4.885 *** | - | - |
Group ADF-stat | - | −3.858 *** | - | - |
Variables | FM-OLS I | FM-OLS II | FM-OLS III |
---|---|---|---|
lnICTExp | 0.232 *** (0.024) | 0.260 *** (0.021) | 0.091 *** (0.011) |
lnICTImp | −0.162 *** (0.046) | −0.172 *** (0.038) | −0.079 *** (0.018) |
lnGDPpc | 0.130 *** (0.035) | 0.132 *** (0.029) | 0.193 *** (0.013) |
lnECI | 0.271 *** (0.069) | 0.273 *** (0.057) | 0.071 *** (0.027) |
lnFDI | −0.002 (0.004) | −0.005 (0.003) | −0.001 (0.002) |
lnRD | −0.670 *** (0.073) | −0.779 *** (0.069) | −0.771 *** (0.031) |
lnTRA | - | 0.160 *** (0.042) | −0.092 *** (0.010) |
lnPOP | - | - | 2.044 *** (0.076) |
Models’ statistics | |||
R2 | 0.955199 | 0.956982 | 0.979361 |
Adjusted-R2 | 0.949599 | 0.951095 | 0.976287 |
Variables | PMG I | PMG II | PMG III |
---|---|---|---|
lnICTExp | 0.582 ***(0.071) | 0.694 *** (0.043) | 0.689 *** (0.055) |
lnICTImp | −0.250 ***(0.105) | −0.475 *** (0.086) | −0.375 *** (0.088) |
lnGDPpc | 0.166 ***(0.025) | 0.227 *** (0.030) | 0.237 *** (0.050) |
lnECI | 0.416 ***(0.128) | 0.461 *** (0.111) | 0.221 *** (0.064) |
lnFDI | 0.081 * (0.043) | 0.050 ** (0.001) | 0.018 (0.002) |
lnRD | −0.177 *** (0.078) | −0.011 *** (0.044) | −0.588 *** (0.065) |
lnTRA | - | 0.068 (0.058) | 0.188 *** (0.028) |
lnPOP | - | - | 2.210 *** (0.153) |
Models’ statistics | |||
Dep: lags | 1 (fixed) | 1 (fixed) | 1 (fixed) |
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Khan, A.; Ximei, W. Digital Economy and Environmental Sustainability: Do Information Communication and Technology (ICT) and Economic Complexity Matter? Int. J. Environ. Res. Public Health 2022, 19, 12301. https://doi.org/10.3390/ijerph191912301
Khan A, Ximei W. Digital Economy and Environmental Sustainability: Do Information Communication and Technology (ICT) and Economic Complexity Matter? International Journal of Environmental Research and Public Health. 2022; 19(19):12301. https://doi.org/10.3390/ijerph191912301
Chicago/Turabian StyleKhan, Asif, and Wu Ximei. 2022. "Digital Economy and Environmental Sustainability: Do Information Communication and Technology (ICT) and Economic Complexity Matter?" International Journal of Environmental Research and Public Health 19, no. 19: 12301. https://doi.org/10.3390/ijerph191912301
APA StyleKhan, A., & Ximei, W. (2022). Digital Economy and Environmental Sustainability: Do Information Communication and Technology (ICT) and Economic Complexity Matter? International Journal of Environmental Research and Public Health, 19(19), 12301. https://doi.org/10.3390/ijerph191912301