Impacts of OFDI on Host Country Energy Consumption and Home Country Energy Efficiency Based on a Belt and Road Perspective
Abstract
:1. Introduction
2. Literature Review
2.1. Motivations for OFDI
2.2. Factors Influencing OFDI
2.3. The Relevance of the Belt and Road Initiative to OFDI and Energy
3. Methodology and Data
3.1. Methodology
3.1.1. DID Model
3.1.2. SNA Model
3.1.3. SYS-GMM
3.2. Data Sources
- (1)
- Considering data availability, only 57 countries along the Belt and Road were studied: Mongolia, Indonesia, Thailand, Malaysia, Vietnam, Singapore, Philippines, Myanmar, Cambodia, Laos, India, Pakistan, Bengal, Sri Lanka, Nepal, Saudi Arabia, United Arab Emirates, Oman, Iran, Turkey, Israel, Egypt, Kuwait, Iraq, Qatar, Jordan, Lebanon, Bahrain, Yemen, Georgia, Azerbaijan, Armenia, Russia, Poland, Romania, Czech, Slovakia, Bulgaria, Hungary, Latvia, Lithuania, Slovenia, Estonia, Croatia, Albania, Serbia, Ukraine, Belarus, Moldova, Macedonia, Bosnia and Herzegovina, Montenegro, Kazakhstan, Uzbekistan, Turkmenistan, Kyrgyzstan, and Tajikistan.
- (2)
- The primary data for the Belt and Road countries came from the World Bank. Market size and economic freedom indexes were from the UNCTAD database. Primary energy data were from the U.S. Energy Agency. The Government Governance Index was calculated from global governance indicators provided by Heritage, combined with the entropy method.
- (3)
- The data of China’s OFDI in 2003–2016 were from the Statistical Bulletin of China’s Foreign Direct Investment. The rest of China’s data were mainly from the China Statistical Yearbook; the China Energy Statistics Yearbook; the China Labor Statistics Yearbook; the China Science and Technology Statistics Yearbook; the China Population and Employment Statistics Yearbook; the China Foreign Economic Statistics Yearbook; the China Statistical Bulletin of Foreign Direct Investment; and the statistical yearbooks of the corresponding years in 30 provinces, municipalities, and autonomous regions. China’s foreign direct investment data came from the UNCTAD database, and R&D expenditures of various countries were from the OECD database.
4. Results and Analysis
4.1. Location Preferences for China’s OFDI
4.2. The Impact of China’s OFDI on the Energy Consumption of Host Countries
4.2.1. Model Specification
4.2.2. Empirical Analysis
4.3. Characteristics of the Spatial Network of Energy Consumption in the Belt and Road Countries
4.4. The Impact of the Reverse Technology Spillover of China’s OFDI on Energy Efficiency
4.4.1. Model Specification
4.4.2. Empirical Analysis
5. Conclusions and Policy Implications
5.1. Conclusions
- As far as the Belt and Road Initiative’s effects are concerned, the current effect on host country energy consumption is not very significant. The possible reasons are (1) political instability in some countries or regions; (2) long lead times for resolving major cooperation projects; and (3) inadequate preparation in multiple areas, including lagging international talent development and inadequate overseas investment protection and insurance mechanisms.
- The energy consumption network density of the Belt and Road countries is only 0.16, and there is much room for improvement. Although a strict hierarchical structure exists in the entire energy consumption space network, some countries’ subordination and marginal status in the space network changed after 2014 [69]. The significant increase in intermediary centrality shows that China’s Belt and Road initiative promotes a connected energy cooperation model. Under the construction of bilateral and multilateral energy cooperation, some developing countries have increased their voice and pricing power.
- Due to the reverse technological spillover effect of OFDI, China’s energy efficiency improved significantly after 2014, but the regional differences were not significant. Regarding the interactive terms, the reverse technology spillover effects of FDI, OFDI, domestic R&D absorptive capacity, human capital, and financial development levels have contributed to China’s energy efficiency.
5.2. Policy Implications
5.2.1. The Macro-Level
5.2.2. The Micro-Level
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Category | Research Objective | Main Conclusion | Main Method | Literature Source |
---|---|---|---|---|
Location choice | Host country | Chinese OFDI is related to energy efficiency, political environment, and resource endowment | PSM-DID | Liu et al. (2020) |
Host country | Chinese OFDI is related to energy efficiency and industrial structure | Generalised Least Squares | Wang et al. (2020) | |
Host country | Chinese OFDI correlates with the host country willingness to participate | OLS Regression Model | Yu et al. (2019) [42] | |
Host country | Chinese OFDI impacts on the environment of host countries | TOPSIS Model | Huang (2019) [43] | |
Energy, carbon emission and environment | Host country | The Belt and Road Initiative promotes carbon leakage through investment. | PSM-DID | Yu et al. (2021) [44] |
Home country, Home country | Pollution caused by Chinese OFDI is influenced by the level of development of the host country. | SYS-GMM | Mahadevan and Sun (2020) | |
Home country | Chinese OFDI helps reduce carbon emissions in host countries | Quantitative Model, Scenario Simulation | Li et al. (2021) | |
Home country | Chinese OFDI helps improve energy security | Logit Model | Zhao et al. (2020) | |
Home country | Fossil energy trade cooperation has a negative impact on the green development capacity of host countries | Spatial Dubin Model | Huang and Li (2020) [45] | |
Technology spillover | Host country | Chinese OFDI is green, and there is no pollution transfer | Threshold Regression Model | Xie and Zhang (2021) [46] |
GDP and capacity | Home country | Chinese OFDI technology spillovers can have an impact on total factor productivity in host countries | Empirical Model | Razzaq (2021) [47] |
Year | Moran I | p |
---|---|---|
2003 | 0.215 | 0.009 |
2004 | 0.294 | 0.001 |
2005 | 0.389 | 0.000 |
2006 | 0.347 | 0.000 |
2007 | 0.293 | 0.000 |
2008 | 0.258 | 0.000 |
2009 | 0.243 | 0.004 |
2010 | 0.271 | 0.002 |
2011 | 0.294 | 0.001 |
2012 | 0.312 | 0.000 |
2013 | 0.329 | 0.000 |
2014 | 0.324 | 0.000 |
2015 | 0.301 | 0.001 |
2016 | 0.307 | 0.002 |
Test Variable | Statistics | p |
---|---|---|
LM (lag) test | 1247.496 | 0.0000 |
Robust LM (lag) test | 215.2227 | 0.0000 |
LM (err) test | 1135.8967 | 0.0000 |
Robust LM (err) test | 103.6225 | 0.0000 |
LR test for spatial lag | 47.743 | 0.0000 |
Wald test for spatial lag | 48.5135 | 0.0000 |
LR test for spatial error | 116.1236 | 0.0000 |
Wald test for spatial error | 108.6035 | 0.0000 |
Variable | Geographic Distance | Economic Distance |
---|---|---|
e | 0.2314 *** (5.3953) | 0.1719 *** (3.9836) |
gdpp | 0.2018 * (1.9153) | 1.1719 *** (7.3217) |
lab | 0.7230 *** (11.0104) | 0.0902 *** (15.0935) |
tra | 0.5114 *** (4.1126) | 0.6148 *** (4.7155) |
ope | 0.0534 *** (5.0141) | 0.0640 *** (5.5749) |
ef | −0.7543 * (−1.5999) | −0.0869(−0.1697) |
gov | 0.3267 * (1.7799) | −0.10369 (−0.5535) |
W *e | 0.0406 (0.2207) | −0.05344 * (−0.2833) |
W *gdp | −0.0822 (−0.1649) | −1.8692 *** (−0.3789) |
W *lab | 2.7236 *** (7.9224) | −0.2005 (−0.7245) |
W *tra | 0.4265 (0.6507) | −1.8939 *** (−4.4963) |
W *ope | 0.0736 (1.1747) | −0.1415 *** (−3.9363) |
W *ef | 8.4509 ** (2.2175) | 2.1970 ** (2.1184) |
W *gov | −2.6759 ** (−3.0845) | −0.45655 (−0.7228) |
W *dep.var | −0.5959 *** (−4.8055) | −0.07879 * (−1.1083) |
R2 | 0.9354 | 0.8895 |
Logl | −1491.9676 | −1544.7915 |
Variable | Geographic Distance | ||
---|---|---|---|
Direct Effect | Indirect Effect | Total Effect | |
e | 0.2328 *** (5.2080) | −0.0543 * (−0.1454) | 0.1784 * (0.4783) |
gdpp | 0.2033 ** (1.9046) | −0.1231 * (−0.3752) | 0.080 * (0.2660) |
lab | 0.6742 *** (8.5685) | 1.5666 (0.6581) | 2.2409 (0.9271) |
tra | 0.5101 *** (4.0592) | 0.0883 ** (4.7155) | 0.5985 * (0.5341) |
ope | 0.5214 *** (4.6137) | 0.0312 *** (0.3073) | 0.0833 * (0.8123) |
ef | −0.9533 ** (−1.9311) | 6.0250 * (0.8574) | −0.7543 (−1.5999) |
gov | 0.3943 ** (2.1772) | −1.9279 (−1.1649) | 0.3267 (1.7799) |
Variable | Economic Distance | ||
Direct Effect | Indirect Effect | Total Effect | |
e | 0.1725 *** (3.9912) | −0.0580 * (−0.3476) | 0.1145 * (0.6232) |
gdpp | 1.1920 *** (7.205) | −1.8343 *** (−3.9937) | −0.6423 * (−1.5340) |
lab | 0.9721 *** (15.1894) | −0.2616 (−1.0939) | 0.7105 *** (2.7277) |
tra | 0.6336 *** (4.8908) | −1.8255 *** (−4.4485) | −1.1918 ** (−2.6361) |
ope | 0.0652 *** (5.7355) | −0.1363 *** (−3.8994) | −0.0711 * (−1.9662) |
ef | −0.1289 (−0.2702) | 2.1410 ** (2.0990) | 2.0221 * (1.8615) |
gov | −0.0889 (−0.4784) | −0.4652 (−0.7855) | −0.5541 (−0.9150) |
Variable | Model 1 | Model 2 | Model 3 | Model 4 |
---|---|---|---|---|
region | 0.0526 *** (1.9280) | 0.0575 ** (2.00) | 0.1354 *** (3.60) | |
time | −0.0204 *** (−1.08) | −0.0187 (−0.50) | −0.2848 *** (−3.79) | |
region × time | −0.0078 (−0.19) | 0.0451 (1.02) | ||
ofdi × region | −0.0225 * (−1.77) | |||
ofdi × time | 0.0514 *** (3.59) | |||
ofdi | 0.0206 *** (3.09) | 0.0223 *** (3.07) | 0.0238 *** (3.31) | 0.0361 *** (3.15) |
gdpp | 0.4401 *** (16.44) | 0.4123 *** (20.17) | 0.4411 *** (16.51) | 0.4397 *** (16.81) |
urb | 1.1187 *** (14.99) | 1.1468 *** (15.23) | 1.1215 *** (14.97) | 1.1174 *** (15.02) |
trade | 0.0769 *** (3.51) | 0.0863 *** (4.08) | 0.0766 *** (3.44) | 0.0787 *** (3.67) |
pop | 0.882 *** (46.57) | 0.8745 *** (46.54) | 0.8786 *** (45.80) | 0.8741 *** (45.75) |
ind | 0.4356 *** (10.87) | 0.4623 *** (12.16) | 0.4269 *** (10.42) | 0.4288 *** (10.31) |
Cons | −8.1990 *** (−40.27) | −8.1232 *** (−39.88) | −8.1830 *** (−39.74) | −8.1935 *** (−40.11) |
R2 | 0.8800 | 0.8796 | 0.8802 | 0.8816 |
F | 740.86 | 727.53 | 579.61 | 499.94 |
Variable | Model 5 | Model 6 | Model 7 |
---|---|---|---|
region | 0.1403 *** (3.71) | 0.4253 *** (4.01) | 0.8051 *** (6.26) |
time | −0.3134 *** (−3.78) | −0.1077 *** (−5.74) | −0.1655 *** (−6.88) |
region × time | 0.3824 (0.87) | 0.0073 (0.71) | −0.0038 (−0.37) |
ofdi × region | −0.0212 * (−1.67) | 0.0156 ** (2.38) | 0.0087 (1.51) |
ofdi × time | 0.0443 *** (3.00) | 0.0177 *** (5.21) | 0.0109 *** (3.05) |
ofdi | 0.4636 *** (3.69) | −0.1703 *** (−0.37) | 0.0066 * (1.18) |
gdpp | 0.4437 *** (17.04) | 0.4150 *** (8.22) | 0.5481 *** (9.39) |
urb | 1.1173 *** (14.86) | 1.5176 *** (8.75) | 0.1775 *** (10.53) |
trade | 0.7826 *** (3.64) | 0.0397 *** (2.77) | 0.0396 *** (2.68) |
pop | 0.8646 *** (43.60) | 1.0713 *** (15.20) | 1.2085 *** (14.52) |
ind | 0.4145 *** (9.83) | 0.0130 (0.47) | −0.0064 (−0.23) |
Cons | −8.0985 (−38.41) | −9.3045 *** (−16.54) | −11.0774 *** (−16.17) |
R2 | 0.8823 | 0.9936 | 0.994 |
F | 243.25 | 9923.30 | 7610.20 |
Individual fixed effects? | NO | YES | YES |
Year fixed effects? | YES | NO | YES |
Variable | Variable | ||
---|---|---|---|
region | 0.0574 ** (2.0) | ofdi × Y2013 | −0.0135 (−0.89) |
time | −0.2512 *** (3.47) | ofdi × Y2014 | 0.0360 * (1.74) |
region × time | 0.01862 (0.46) | ofdi × Y2015 | 0.0372 * (1.81) |
ofdi × Y2004 | −0.0054 (−0.30) | ofdi × Y2016 | 0.0366 * (1.79) |
ofdi × Y2005 | −0.0076 (−0.44) | ofdi | 0.0327 *** (1.97) |
ofdi × Y2006 | −0.0106 (−0.64) | ind | 0.4402 *** (16.58) |
ofdi × Y2007 | −0.0107 (−0.66) | gdpp | 1.1284 *** (15.00) |
ofdi × Y2008 | −0.0144 (−0.91) | urb | 0.0762 *** (3.54) |
ofdi × Y2009 | −0.0130 (−0.84) | trade | 0.8704 *** (44.33) |
ofdi × Y2010 | −0.0121 (−0.79) | pop | 0.4167 *** (10.05) |
ofdi × Y2011 | −0.0140 (−0.91) | Cons | −8.1124 *** (−39.03) |
ofdi × Y2012 | −0.0126 (−0.82) |
Time | M | WD | WR | WH | WE |
---|---|---|---|---|---|
2003 | 534 | 0.1615 | 1 | 0.3235 | 0.7581 |
2004 | 519 | 0.1570 | 1 | 0.3235 | 0.8033 |
2005 | 522 | 0.1579 | 1 | 0.3235 | 0.8033 |
2006 | 523 | 0.1582 | 1 | 0.3235 | 0.8033 |
2007 | 519 | 0.1570 | 1 | 0.3235 | 0.8058 |
2008 | 523 | 0.1582 | 1 | 0.3235 | 0.8064 |
2009 | 519 | 0.1570 | 1 | 0.3240 | 0.8064 |
2010 | 515 | 0.1558 | 1 | 0.3240 | 0.8058 |
2011 | 498 | 0.1506 | 1 | 0.3478 | 0.8158 |
2012 | 512 | 0.1549 | 1 | 0.3235 | 0.8089 |
2013 | 512 | 0.1549 | 1 | 0.3478 | 0.8095 |
2014 | 518 | 0.1567 | 1 | 0.3478 | 0.8087 |
2015 | 527 | 0.1594 | 1 | 0.3235 | 0.8045 |
2016 | 529 | 0.1600 | 1 | 0.2985 | 0.8039 |
Country | 2003 | 2014 | 2016 | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Closeness Centrality | Betweenness Centrality | Degree Centrality | Ranking | In-Degree | Out-Degree | Closeness Centrality | Betweenness Centrality | Degree Centrality | Ranking | In-Degree | Out-Degree | Closeness Centrality | Betweenness Centrality | Degree Centrality | Ranking | In-Degree | Out-Degree | |
China | 0.4694 | 19.7466 | 10 | 42 | 1 | 9 | 0.4423 | 0.0000 | 9 | 43 | 0 | 9 | 0.4476 | 182.6269 | 13 | 34 | 3 | 10 |
Mongolia | 0.4796 | 0.0000 | 9 | 45 | 0 | 9 | 0.5055 | 0.0000 | 10 | 39 | 0 | 10 | 0.5275 | 0.0000 | 12 | 37 | 0 | 12 |
Indonesia | 0.2150 | 0.0000 | 5 | 57 | 3 | 2 | 0.1731 | 0.0000 | 5 | 57 | 3 | 2 | 0.1950 | 0.0000 | 5 | 56 | 3 | 2 |
Thailand | 0.3358 | 94.8143 | 14 | 30 | 6 | 8 | 0.2446 | 161.3333 | 11 | 35 | 6 | 5 | 0.2883 | 224.8377 | 12 | 36 | 6 | 6 |
Malaysia | 0.2150 | 3.0000 | 11 | 38 | 9 | 2 | 0.1731 | 3.0000 | 11 | 34 | 9 | 2 | 0.1950 | 3.3333 | 12 | 35 | 10 | 2 |
Vietnam | 0.3485 | 60.9659 | 10 | 41 | 2 | 8 | 0.3000 | 41.1667 | 9 | 42 | 2 | 7 | 0.3643 | 117.6192 | 10 | 41 | 2 | 8 |
Singapore | 0.2706 | 390.8444 | 24 | 12 | 19 | 5 | 0.2074 | 381.9167 | 25 | 11 | 21 | 4 | 0.2398 | 396.1375 | 25 | 12 | 21 | 4 |
Philippines | 0.3151 | 32.4143 | 7 | 55 | 1 | 6 | 0.2473 | 69.4167 | 6 | 54 | 1 | 5 | 0.2670 | 48.5505 | 6 | 55 | 1 | 5 |
Myanmar | 0.3511 | 5.4500 | 10 | 40 | 1 | 9 | 0.2514 | 3.4167 | 7 | 50 | 1 | 6 | 0.2765 | 3.6111 | 7 | 51 | 1 | 6 |
Cambodia | 0.3154 | 0.0000 | 7 | 54 | 0 | 7 | 0.2421 | 0.0000 | 5 | 56 | 0 | 5 | 0.2474 | 0.3333 | 6 | 54 | 1 | 5 |
Laos | 0.3481 | 0.0000 | 7 | 53 | 0 | 7 | 0.2541 | 0.0000 | 6 | 53 | 0 | 6 | 0.2791 | 0.0000 | 6 | 53 | 0 | 6 |
India | 0.4381 | 366.7906 | 16 | 25 | 5 | 11 | 0.4545 | 938.6546 | 17 | 23 | 7 | 10 | 0.4653 | 646.7229 | 18 | 22 | 7 | 11 |
Pakistan | 0.3680 | 250.9241 | 12 | 35 | 3 | 9 | 0.4128 | 90.8597 | 14 | 30 | 4 | 10 | 0.4052 | 105.1311 | 15 | 28 | 6 | 9 |
Bengal | 0.3594 | 2.0095 | 11 | 37 | 1 | 10 | 0.3285 | 5.8429 | 12 | 33 | 2 | 10 | 0.4234 | 11.0987 | 13 | 33 | 2 | 11 |
Sri Lanka | 0.3456 | 0.0000 | 7 | 52 | 0 | 7 | 0.3046 | 0.0000 | 7 | 49 | 0 | 7 | 0.3097 | 0.0000 | 8 | 46 | 0 | 8 |
Nepal | 0.4393 | 0.0000 | 9 | 44 | 0 | 9 | 0.3239 | 0.0000 | 9 | 41 | 0 | 9 | 0.3582 | 0.0000 | 9 | 44 | 0 | 9 |
Saudi Arabia | 0.3239 | 231.1935 | 38 | 4 | 31 | 7 | 0.3383 | 724.4516 | 31 | 6 | 24 | 7 | 0.3431 | 478.8762 | 31 | 6 | 24 | 7 |
United Arab Emirates | 0.2987 | 95.2185 | 18 | 18 | 13 | 5 | 0.2586 | 15.0333 | 19 | 20 | 16 | 3 | 0.2611 | 17.6195 | 19 | 20 | 16 | 3 |
Oman | 0.3433 | 212.9054 | 24 | 11 | 18 | 6 | 0.3383 | 62.1782 | 15 | 27 | 9 | 6 | 0.3431 | 44.3638 | 15 | 27 | 9 | 6 |
Iran | 0.2893 | 3.1401 | 12 | 34 | 6 | 6 | 0.2695 | 2.5333 | 9 | 40 | 4 | 5 | 0.3333 | 136.5660 | 9 | 43 | 3 | 6 |
Turkey | 0.4381 | 597.2394 | 24 | 10 | 14 | 10 | 0.4592 | 429.1606 | 27 | 8 | 15 | 12 | 0.4563 | 430.3180 | 30 | 7 | 18 | 12 |
Israel | 0.2140 | 155.0000 | 47 | 3 | 45 | 2 | 0.1829 | 142.8083 | 43 | 3 | 41 | 2 | 0.1808 | 142.4164 | 42 | 2 | 40 | 2 |
Egypt | 0.2875 | 27.8803 | 12 | 33 | 7 | 5 | 0.2695 | 26.9778 | 13 | 31 | 8 | 5 | 0.2717 | 29.8303 | 14 | 29 | 9 | 5 |
Kuwait | 0.3382 | 600.5419 | 53 | 2 | 47 | 6 | 0.2679 | 208.7239 | 45 | 2 | 39 | 6 | 0.2901 | 166.0291 | 40 | 3 | 34 | 6 |
Iraq | 0.2875 | 2.7949 | 8 | 49 | 3 | 5 | 0.2679 | 2.5333 | 7 | 48 | 3 | 4 | 0.2701 | 2.3763 | 7 | 50 | 3 | 4 |
Qatar | 0.2644 | 105.2250 | 53 | 1 | 49 | 4 | 0.2647 | 292.7432 | 54 | 1 | 50 | 4 | 0.2670 | 281.4095 | 54 | 1 | 50 | 4 |
Jordan | 0.1769 | 0.0000 | 3 | 58 | 2 | 1 | 0.1552 | 0.0000 | 4 | 58 | 3 | 1 | 0.1536 | 0.0000 | 4 | 58 | 3 | 1 |
Lebanon | 0.2690 | 88.0000 | 5 | 56 | 1 | 4 | 0.2217 | 86.0000 | 5 | 55 | 1 | 4 | 0.2186 | 90.0000 | 4 | 57 | 1 | 3 |
Bahrain | 0.2421 | 8.8260 | 23 | 13 | 20 | 3 | 0.2163 | 3.8333 | 16 | 26 | 13 | 3 | 0.2176 | 3.1929 | 15 | 26 | 12 | 3 |
Yemen | 0.3154 | 0.0000 | 7 | 51 | 0 | 7 | 0.2805 | 0.0000 | 7 | 47 | 0 | 7 | 0.2824 | 0.0000 | 7 | 49 | 0 | 7 |
Georgia | 0.3983 | 0.0000 | 8 | 48 | 0 | 8 | 0.4107 | 0.0000 | 8 | 44 | 0 | 8 | 0.4103 | 0.0000 | 9 | 42 | 0 | 9 |
Azerbaijan | 0.3643 | 0.0000 | 8 | 47 | 0 | 8 | 0.3382 | 0.0000 | 6 | 52 | 0 | 6 | 0.3357 | 0.0000 | 6 | 52 | 0 | 6 |
Armenia | 0.3983 | 0.0000 | 8 | 46 | 0 | 8 | 0.3866 | 0.0000 | 7 | 46 | 0 | 7 | 0.4068 | 0.0000 | 8 | 45 | 0 | 8 |
Russia | 0.5000 | 295.3491 | 17 | 19 | 6 | 11 | 0.5357 | 473.1060 | 17 | 22 | 4 | 13 | 0.5281 | 445.8616 | 19 | 19 | 6 | 13 |
Poland | 0.3898 | 21.2588 | 22 | 14 | 11 | 11 | 0.5000 | 101.4327 | 24 | 12 | 11 | 13 | 0.4796 | 125.4089 | 25 | 11 | 13 | 12 |
Romania | 0.4220 | 14.4214 | 20 | 17 | 7 | 13 | 0.4455 | 162.1185 | 26 | 10 | 13 | 13 | 0.4352 | 151.7289 | 25 | 10 | 12 | 13 |
Czech | 0.4554 | 372.2629 | 35 | 5 | 20 | 15 | 0.4327 | 254.4116 | 32 | 4 | 18 | 14 | 0.4608 | 313.4402 | 37 | 4 | 22 | 15 |
Slovakia | 0.4035 | 36.3220 | 27 | 8 | 13 | 14 | 0.4369 | 70.9214 | 31 | 5 | 16 | 15 | 0.4273 | 64.2363 | 31 | 5 | 16 | 15 |
Bulgaria | 0.4071 | 9.4107 | 16 | 24 | 5 | 11 | 0.4286 | 14.8274 | 19 | 19 | 7 | 12 | 0.4234 | 14.5863 | 19 | 18 | 7 | 12 |
Hungary | 0.4071 | 43.0313 | 28 | 7 | 13 | 15 | 0.4128 | 38.0238 | 26 | 9 | 13 | 13 | 0.4017 | 42.5756 | 27 | 9 | 13 | 14 |
Latvia | 0.3833 | 0.3790 | 11 | 36 | 2 | 9 | 0.3947 | 1.2986 | 12 | 32 | 3 | 9 | 0.3790 | 1.0731 | 11 | 40 | 3 | 8 |
Lithuania | 0.3802 | 1.3373 | 12 | 32 | 4 | 8 | 0.3543 | 1.9835 | 7 | 45 | 3 | 4 | 0.3456 | 1.5852 | 7 | 48 | 3 | 4 |
Slovenia | 0.3382 | 298.6170 | 34 | 6 | 24 | 10 | 0.3462 | 55.8261 | 27 | 7 | 18 | 9 | 0.3643 | 50.3766 | 27 | 8 | 18 | 9 |
Estonia | 0.4554 | 11.4176 | 16 | 23 | 3 | 13 | 0.4054 | 6.9851 | 14 | 29 | 3 | 11 | 0.4393 | 11.0311 | 13 | 32 | 3 | 10 |
Croatia | 0.3087 | 6.0092 | 15 | 27 | 11 | 4 | 0.3409 | 3.6318 | 16 | 25 | 10 | 6 | 0.3534 | 3.7924 | 16 | 25 | 10 | 6 |
Albania | 0.4071 | 39.7420 | 16 | 22 | 6 | 10 | 0.4327 | 47.8730 | 20 | 16 | 7 | 13 | 0.4273 | 45.5574 | 20 | 15 | 7 | 13 |
Serbia | 0.3740 | 12.7755 | 16 | 21 | 7 | 9 | 0.4206 | 24.0899 | 20 | 15 | 9 | 11 | 0.4123 | 15.3942 | 18 | 21 | 8 | 10 |
Ukraine | 0.4466 | 128.7834 | 24 | 9 | 10 | 14 | 0.4688 | 195.2176 | 23 | 13 | 10 | 13 | 0.4563 | 168.7201 | 24 | 13 | 11 | 13 |
Belarus | 0.4466 | 116.6673 | 20 | 16 | 8 | 12 | 0.4688 | 155.7777 | 19 | 18 | 7 | 12 | 0.4434 | 120.1162 | 19 | 17 | 8 | 11 |
Moldova | 0.4259 | 35.7197 | 21 | 15 | 8 | 13 | 0.4455 | 60.8953 | 22 | 14 | 8 | 14 | 0.4393 | 67.4001 | 22 | 14 | 8 | 14 |
Macedonia | 0.4144 | 17.7680 | 15 | 26 | 3 | 12 | 0.4327 | 21.4212 | 19 | 17 | 6 | 13 | 0.4273 | 21.1117 | 19 | 16 | 6 | 13 |
Bosnia and Herzegovina | 0.3770 | 38.6167 | 16 | 20 | 7 | 9 | 0.3982 | 31.8028 | 16 | 24 | 7 | 9 | 0.3950 | 30.5076 | 16 | 24 | 7 | 9 |
Montenegro | 0.3833 | 1.4000 | 14 | 29 | 3 | 11 | 0.3750 | 2.3876 | 14 | 28 | 3 | 11 | 0.3790 | 1.2310 | 13 | 31 | 3 | 10 |
Kazakhstan | 0.4381 | 188.2981 | 14 | 28 | 4 | 10 | 0.3782 | 198.1370 | 17 | 21 | 8 | 9 | 0.4273 | 171.8638 | 17 | 23 | 8 | 9 |
Uzbekistan | 0.5227 | 17.1219 | 13 | 31 | 1 | 12 | 0.4091 | 5.5402 | 10 | 38 | 1 | 9 | 0.5165 | 25.1090 | 13 | 30 | 1 | 12 |
Turkmenistan | 0.3154 | 0.0000 | 7 | 50 | 0 | 7 | 0.2968 | 0.0000 | 6 | 51 | 0 | 6 | 0.3404 | 0.0000 | 7 | 47 | 0 | 7 |
Kyrgyzstan | 0.4694 | 15.3667 | 10 | 39 | 1 | 9 | 0.4091 | 5.7069 | 10 | 37 | 1 | 9 | 0.5000 | 15.2924 | 11 | 39 | 1 | 10 |
Tajikistan | 0.3701 | 0.0000 | 9 | 43 | 0 | 9 | 0.4144 | 0.0000 | 10 | 36 | 0 | 10 | 0.5053 | 0.0000 | 11 | 38 | 0 | 11 |
Variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 |
---|---|---|---|---|---|---|
LnEI(-1) | 0.2724 ***(7.23) | 0.2587 *** (8.96) | 0.2864 *** (6.97) | 0.3142 *** (10.19) | 0.2441 *** (8.32) | 0.24449 *** (8.41) |
time | −0.2079 *** (−2.21) | |||||
region | 0.0419 (0.25) | |||||
Lnofdi × time | 0.1954 * (1.17) | |||||
Lnofdi × region | −0.0105 (−1.21) | |||||
Lngdp | 0.1909 *** (4.17) | 0.2105 *** (3.42) | 0.2534 *** (9.52) | 0.1849 *** (5.01) | 0.1737 *** (5.15) | 0.1814 *** (2.60) |
Lnes | 0.2273 ** (2.42) | 0.2389 *** (6.68) | 0.3212 ** (3.83) | 0.2586 *** (6.82) | 0.2566 *** (4.30) | 0.2744 *** (5.57) |
Lnis | 0.1999 *** (4.19) | 0.1576 *** (3.99) | 0.1710 *** (5.36) | 0.1237 *** (3.72) | 0.1864 *** (2.58) | 0.1497 *** (3.72) |
Lnurb | 0.3892 * (1.85) | 0.6679 ** (2.17) | 0.3630 *** (2.79) | 0.6010 *** (4.54) | 0.6300 *** (3.11) | 0.5735 * (1.86) |
Lnofdi | 0.0536 *** (4.54) | |||||
Lnofdi × fdi | 0.0033 *** (3.28) | |||||
Lnofdi × ds | −0.0034 *** (−3.79) | |||||
Lnofdi × rd | 0.0031 *** (3.96) | |||||
Lnofdi × hc | 0.0167 *** (5.82) | |||||
Lnofdi × fin | 0.0038 *** (4.54) | |||||
Cons | −5.0587 *** (−4.63) | −6.2043 *** (−7.82) | −5.1924 *** (−8.41) | −5.5226 *** (−10.34) | −5.7440 *** (−7.55) | −5.5599 *** (−6.47) |
sargen p | 1 | 1 | 1 | 1 | 1 | 1 |
AR(1) | 0.0124 | 0.0086 | 0.0091 | 0.012 | 0.0118 | 0.0121 |
AR(2) | 0.1796 | 0.1249 | 0.1330 | 0.1279 | 0.1719 | 0.1488 |
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Zhou, X.; Guo, Q.; Zhang, M. Impacts of OFDI on Host Country Energy Consumption and Home Country Energy Efficiency Based on a Belt and Road Perspective. Energies 2021, 14, 7343. https://doi.org/10.3390/en14217343
Zhou X, Guo Q, Zhang M. Impacts of OFDI on Host Country Energy Consumption and Home Country Energy Efficiency Based on a Belt and Road Perspective. Energies. 2021; 14(21):7343. https://doi.org/10.3390/en14217343
Chicago/Turabian StyleZhou, Xing, Quan Guo, and Ming Zhang. 2021. "Impacts of OFDI on Host Country Energy Consumption and Home Country Energy Efficiency Based on a Belt and Road Perspective" Energies 14, no. 21: 7343. https://doi.org/10.3390/en14217343
APA StyleZhou, X., Guo, Q., & Zhang, M. (2021). Impacts of OFDI on Host Country Energy Consumption and Home Country Energy Efficiency Based on a Belt and Road Perspective. Energies, 14(21), 7343. https://doi.org/10.3390/en14217343