4.2. Panel ARDL Analysis
Table 3 displays the result of the panel ARDL approach. Particularly, a 1% improvement in lnR leads to reduces lnEF by 0.065%. It indicates that using renewable (clean) energy has a negative considerable impact on environment. Using various types of non-conventional (clean) energies namely solar, wind, biomass, water, and geothermal energy, all helpful to minimize CO2 emissions, decrease the ecological footprints as well as promote environmental quality. This result is the same as the result of (Mehmood, 2021) for the 15 Highest Emitting Nations, (Sharif et al., 2020) for Turkey, (O. Usman et al., 2020) for USA, (Sharma et al., 2021) for Southeast Asia nations, (Zhang et al., 2021) for remittance receiving countries, and (Nathaniel & Khan, 2020) for ASEAN nations. The findings confirmed that the utilization of non-renewable energy considerably increases the ecological footprint. Precisely, a 1% intensification in lnNR contributes to an increase in lnEF by 0.131%. It indicates that ecological footprints and non-renewable energy, both have a positive association. This similar finding is recognized by (Raghutla & Chittedi, 2020) for BRICS nations, (Salahuddin et al., 2015) for GCC nations, and (Chindo et al., 2015) for EU countries, (Abbas et al., 2021) for Pakistan, (Saidi & Hammami, 2015) for Global panel nations, (Ulucak & Khan, 2020) for Pakistan, and (Ozturk & Acaravci, 2010) for Turkey. The estimated ARDL coefficients shows that both economic growth as well as ecological footprints are positively associated, this result indicates that higher economic growth increases more ecological footprints. The analysis revealed that a 1% rise in lnY result in a 0.250% increase in lnEF. Because most of the countries are directly dependent on the conventional energy use and this leads to produces more ecological footprints along with growth. RECAI countries are highly using conventional energy (i.e., oil and coal) in the different production industries which is leads to higher in ecological footprints. Therefore, the countries need to lower the usage of conventional (traditional) energy not only in the production industries but also require to extend the other sectors as well. The same results were found by previous authors namely (Hassan et al., 2019) for Pakistan, (Galli et al., 2012) for China and India, (Ahmed et al., 2020) for China, and (Kirikkaleli et al., 2021) for Turkey. Finally, this result also affirmed that trade openness has a significant harmful effect on the environment, a 1% improvement in lnT result in a 0.136% decrease in lnEF. International trade removes the barriers to transferring technology which is provided with the nations to access the cleaner technologies. It reduced the level of ecological footprint; as a result, trade openness significantly curbs the ecological footprints. The same results were found by previous authors namely (Destek & Sinha, 2020) for economic cooperation and developed countries, (Destek et al., 2018) for EU nations, (Charfeddine, 2017) for GCC and MENA countries, and (Lu, 2020) for Asian countries.
The ECM coefficient is negative in the short term, with a value of -0.389 and 1% level of significance. In the short-term, particularly, the findings display that both renewable (clean) energy and non-renewable energy utilization have a negative and substantial impact on the ecological footprint but its coefficients values are not significant. In RECAI countries, if economic growth improvement by 1%, ecological footprints will increase by 0.674%, moreover, coefficients are significant particularly at 1% level. This finding also proposes that ecological footprints and trade openness have a negative relationship, particularly, if trade openness improvement by 1%, ecological footprint will cause to increase by 0.210% and coefficients are significant at a 1% level in the short-term.
In the RECAI nations, the ARDL method, results clearly show that renewable energy use as well as trade openness improve quality of environment but conventional energy usage and economic growth do not. This could be due to the extensive utilization of conventional energy in the process of industrial production and other based needs. Overcome this problem is only possible by the reduction of conventional energy use; therefore, RECAI countries need to replace it with non-conventional energy resources like solar, biomass, wind, geothermal and hydropower etc. The governments of RECAI countries have to take the necessary actions particularly fund allocation for renewable energy projects and also encourage private participation in renewable energy projects by providing various incentives. Along with this, RECAI countries need to bring the environmental policies and reframe their energy policies which are need to give more importance to renewable energy projects. Further, it can dampen the ecological footprint in RECAI countries and considerably enhance the quality of environment.
Table 3
Results of panel ARDL model
\(EF=f(Y,R,NR,T)\) |
Long-run results |
Variable | Coef. | Std. Error | t-stat | Prob. |
R | -0.065* | 0.004 | -14.454 | 0.000 |
NR | 0.131* | 0.020 | 6.555 | 0.000 |
Y | 0.250* | 0.016 | 15.471 | 0.000 |
T | -0.136* | 0.014 | -9.226 | 0.000 |
Short-run results |
Variable | Coef. | Std. Error | t-stat | Prob. |
COINTEQ01 | -0.389 | 0.108 | -3.592 | 0.000* |
D(EF(-1)) | -0.135 | 0.091 | -1.485 | 0.138 |
D(EF(-2)) | 0.022 | 0.075 | 0.298 | 0.765 |
D(EF(-3)) | -0.187 | 0.250 | -0.745 | 0.456 |
D(R) | -0.213 | 0.153 | -1.394 | 0.164 |
D(R(-1)) | -0.049 | 0.111 | -0.441 | 0.658 |
D(R(-2)) | 0.206 | 0.174 | 1.184 | 0.237 |
D(R(-3)) | 0.072 | 0.123 | 0.584 | 0.559 |
D(NR) | 0.685 | 0.431 | 1.589 | 0.112 |
D(NR(-1)) | 0.251 | 0.845 | 0.297 | 0.766 |
D(NR(-2)) | 1.432 | 1.341 | 1.067 | 0.286 |
D(NR(-3)) | 1.336 | 0.943 | 1.417 | 0.157 |
D(Y) | 0.674* | 0.253 | 2.663 | 0.008 |
D(Y(-1)) | 0.083 | 0.286 | 0.290 | 0.772 |
D(Y(-2)) | 0.675*** | 0.381 | 1.771 | 0.077 |
D(Y(-3)) | 0.074 | 0.212 | 0.352 | 0.724 |
D(T) | 0.210* | 0.085 | 2.454 | 0.014 |
D(T(-1)) | 0.113 | 0.068 | 1.645 | 0.100 |
D(T(-2)) | 0.006 | 0.082 | 0.084 | 0.932 |
D(T(-3)) | -0.018 | 0.063 | -0.292 | 0.770 |
C | -1.780 | 0.555 | -3.207 | 0.001* |
Note
*, *** indicates one and ten percentage significant levels.
4.4. Long-run Analysis of Individuals Nations
Table 5 demonstrates the result of individual nations which is estimated by utilizing the DOLS method. The main reason for individual countries' analysis, is we have to know the performance of individual countries towards reduction of ecological footprints which is more useful for policymakers of nations. RECAI countries mainly consume and produce renewable energy and we have to know the significant level of utilization and to what extent those nations have curbed their ecological footprints. Therefore, we estimated individual nations, long-run elasticities. Particularly, ecological footprints in relation to growth are considerable positive for Chile (0.183343), China (1.001764), Denmark (0.053561), Greece (0.360591), Ireland (0.155179), Japan (0.073294), Korea Rep. (0.362029), Morocco (0.567448), Netherlands (0.599342), Romania (0.237314), Saudi Arabia (1.026827) and Turkey (0.192589). For these 12 countries, economic growth improvement has a substantial direct impact on ecological footprints. This empirical analysis indicates that economic growth will significantly add more ecological footprints to the environment due to more conventional energy usage in the process of production. This finding is the same (Hassan et al., 2019) for Pakistan, (Galli et al., 2012) for India and China, (Ahmed et al., 2020) for China, and (Kirikkaleli et al., 2021) for Turkey. However, long-run ecological footprints elasticities also disclose the substantial negative impact of growth of RECAI nations on the environmental quality which is revealed for seven countries namely Australia (-0.171622), Austria (-0.254069), Canada (-0.029541), Germany (-0.511752), Norway (-0.230284), South Africa (-0.382278) and United States (-0.240143). This result shows that these seven nations started utilization of renewable energy in their nations in place of conventional energy in their production processes, which has improved quality of environment. This evidence is same to that of (Raghutla et al., 2021) for major investment nations and (Hu et al., 2021) for India. Furthermore, the economic growth has both the positive and negative impact, but statistically insignificant impact on ecological footprints for 20 countries, particularly, for Belgium (0.106391), Brazil (0.024040), Czech Rep. (0.074502), India (0.035208), Mexico (0.307027), Peru (0.026872), Poland (0.181681), Portugal (0.091672), Slovenia (0.128535), Spain (0.204402), Sweden (0.031784), Ukraine (0.158640), Bulgaria (-0.235712), Finland (-0.301248), France (-0.022195), Israel (-0.006967), Italy (-0.131862), Kenya (-0.081147), Thailand (-0.559202) and United Kingdom (-0.076811). This evidence substantially indicates that utilization of renewable energy is at early stage in the process of production and also follows the energy mix.
The long-run elasticities of ecological footprints in relation to utilization of renewable energy, are significant negative for Australia (-0.665953), Belgium (-0.094025), Chile (-0.200933), Denmark (-0.239666), France (-0.192670), Greece (-0.625309), India (-0.541907), Ireland (-0.221611), Korea Rep. (-0.074882), Morocco (-0.179446), Netherlands (-0.219322), Peru (-0.364033), Portugal (-0.342057), Romania (-0.288313), South Africa (-0.312063), Spain (-0.609967), Turkey (-0.303971) and Ukraine (-0.185441). For 18 RECAI countries, renewable energy usage has an inverse considerable effect on environmental footprints. The usage of renewable energy, according to this empirical analysis, will greatly improve environmental quality by reducing ecological footprints. This result is consistent with earlier research namely (Mehmood, 2021) for the 15 Highest Emitting Countries, (Sharif et al., 2020) for the case of Turkey, (O. Usman et al., 2020) for USA, (Sharma et al., 2021) for Southeast Asia nations, (Zhang et al., 2021) for Remittance receiving countries, and (Nathaniel & Khan, 2020) for ASEAN Countries. However, the long-run elasticities show the direct effect of renewable (clean) energy utilization on environmental footprints, which is affirmed for Austria (0.458583) and Norway (0.845168). These two countries still depend on the more conventional energy sources which leads to an increase in ecological footprints. Therefore, these two countries need to replace particularly conventional energy in place of renewable energy sources to reduction of ecological footprints. This analysis is the same as that of (Nathaniel et al., 2020) for CIVETS countries. In addition, the renewable energy usage has a direct and negative impact on environmental footprints in 19 countries, but coefficients values are statistically insignificant, particularly, Bulgaria (0.098833), Finland (1.722640), Germany (0.084060), Kenya (0.073561), Mexico (0.087784), Thailand (0.749077) and United States (0.114915), while, Brazil (-0.067405), Canada (-0.015636), China (-0.064087), Czech Rep. (-0.994379), Israel (-0.052705), Italy (-0.027891), Japan (-0.103397), Poland (-0.056451), Saudi Arabia (-0.062993), Slovenia (-0.410748), Sweden (-0.071500) and United Kingdom (-0.047636). This analysis evidence that these 19 nations early stage of renewable energy utilization.
The long-run elasticities, particularly ecological footprints in relation to conventional or non-renewable energy usage, are significant for six countries namely Australia (0.767181), Canada (0.554320), France (0.651401), Germany (3.591729), South Africa (2.558199) and United States (1.910811), this suggests that utilization of non-renewable energy will considerably generate the ecological footprints. Therefore, to reduce their ecological footprints, these six countries must expand their use of renewable energy. This evidence is recognized by (Raghutla & Chittedi, 2020) for BRICS nations, (Salahuddin et al., 2015) for the gulf cooperation council, and (Chindo et al., 2015) for EU nations, (Abbas et al., 2021) for Pakistan, (Saidi & Hammami, 2015) for Global panel countries, (Ulucak & Khan, 2020) for Pakistan, and (Ozturk & Acaravci, 2010) for the case of Turkey economy. In contrast, the long-run elasticities show the negative impact of utilization of non-renewable or conventional energy on ecological footprints, which is significantly affirmed for ten different countries namely Belgium (-1.080421), Chile (-0.583265), China (-6.200239), Greece (-1.483880), Korea Rep. (-1.781583), Morocco (-2.712848), Netherlands (-2.837685), Romania (-0.828651), Saudi Arabia (-5.976091) and Turkey (-0.642183). It is indicating that these ten countries have not only significantly adopted carbon capture technology but have also increase their usage of renewable energy which significantly lowers the ecological footprints. This result is the same as earlier findings that of (Acheampong, 2018) for MENA, (Li et al., 2020) for China, (Zou & Zhang, 2020) for China, and (Svedberg, 2021) for OECD countries. The non-renewable or traditional energy usage has a both positive and negative but statistically insignificant effect on ecological footprints for 23 countries mainly Austria (0.407919), Brazil (0.240165), Bulgaria (1.519572), Czech Rep. (0.110697), Denmark (0.000467), Finland (1.049226), India (0.239507), Israel (0.092793), Italy (1.124164), Japan (0.045196), Kenya (0.432077), Norway (0.659952), Peru (0.187332), Portugal (0.103684) Sweden (0.170439), Thailand (2.265330), and United Kingdom (1.025801), while Ireland (-0.363577), Mexico (-1.667034), Poland (-0.586811), Slovenia (-0.513299), Spain (-0.661970) and Ukraine (-0.511889). This analysis suggests that these 23 countries can significantly shows the quality of environment improvement when increase in renewable energy share which is a major strategy for climate change and environmental sustainability.
The long-run elasticities show the positive considerable effect of trade openness on ecological footprints, which is considerably affirmed for nine countries namely Australia (1.521857), Belgium (0.776539), Canada (0.176009), Greece (0.113260), Israel (0.437041), Norway (0.498667), Peru (0.158328), Saudi Arabia (0.292289) and Sweden (0.175702). This empirical evidence implies that trade openness increases ecological footprints. This finding is similar to (Al-Mulali et al., 2015) for ninety three nations, (Al-mulali et al., 2016) for 58 countries, (Aşici & Acar, 2015) for 116 nations, (Kongbuamai et al., 2020) for Thailand and (Ozturk et al., 2016) for 144 countries. However, the long-run empirical elasticities of ecological footprints in relation to trade openness, are significant negative for four countries particularly Brazil (-0.140466), Japan (-0.219299), Morocco (-0.343332), and Turkey (-0.135102). This analysis evidence that increasing trade openness reduces environmental impact via the exchange of technological innovations. This empirical evidence same as that of (Destek & Sinha, 2020) for economic cooperation and developed countries, (Destek et al., 2018) for EU nations, (Charfeddine, 2017) for GCC and MENA countries, (Aydin & Turan, 2020) for BRICS countries. For 26 countries, trade openness significantly has both beneficial and unfavorable effects in the environment, but these effects are statistically insignificant, specifically, Austria (0.249136), Bulgaria (0.076275), Czech Rep. (0.414014), Denmark (0.337752), France (0.051734), Germany (0.105519), India (0.007031), Italy (0.117378), Kenya (0.177215), Slovenia (0.418659), South Africa (0.199467), Spain (0.072806), and Thailand (0.795743), while Chile (-0.063882), China (-0.173344), Finland (-0.079236), Ireland (-0.080919), Korea Rep. (-0.123008), Mexico (-0.055472), Netherlands (-0.337172), Poland (-0.154949), Portugal (-0.084911), Romania (-0.155023), Ukraine (-0.130195), United Kingdom (-0.206886) and United States (-0.048103). This analysis suggests that these 26 countries need to improve their trade openness for reduction of ecological footprints.
Table 5
Results of long-run ecological footprint elasticities using the DOLS Model (Dependent variable: ecological footprints).
Variable | Y | R | NR | T |
Australia | -0.171622* | -0.665953* | 0.767181* | 1.521857* |
Austria | -0.254069*** | 0.458583* | 0.407919 | 0.249136 |
Belgium | 0.106391 | -0.094025* | -1.080421** | 0.776539* |
Brazil | 0.024040 | -0.067405 | 0.240165 | -0.140466* |
Bulgaria | -0.235712 | 0.098833 | 1.519572 | 0.076275 |
Canada | -0.029541*** | -0.015636 | 0.554320* | 0.176009** |
Chile | 0.183343* | -0.200933** | -0.583265** | -0.063882 |
China | 1.001764* | -0.064087 | -6.200239** | -0.173344 |
Czech Rep. | 0.074502 | -0.994379 | 0.110697 | 0.414014 |
Denmark | 0.053561*** | -0.239666* | 0.000467 | 0.337752 |
Finland | -0.301248 | 1.722640 | 1.049226 | -0.079236 |
France | -0.022195 | -0.192670* | 0.651401* | 0.051734 |
Germany | -0.511752* | 0.084060 | 3.591729* | 0.105519 |
Greece | 0.360591* | -0.625309* | -1.483880* | 0.113260** |
India | 0.035208 | -0.541907* | 0.239507 | 0.007031 |
Ireland | 0.155179** | -0.221611* | -0.363577 | -0.080919 |
Israel | -0.006967 | -0.052705 | 0.092793 | 0.437041* |
Italy | -0.131862 | -0.027891 | 1.124164 | 0.117378 |
Japan | 0.073294** | -0.103397 | 0.045196 | -0.219299* |
Kenya | -0.081147 | 0.073561 | 0.432077 | 0.177215 |
Korea Rep. | 0.362029* | -0.074882* | -1.781583* | -0.123008 |
Mexico | 0.307027 | 0.087784 | -1.667034 | -0.055472 |
Morocco | 0.567448* | -0.179446* | -2.712848* | -0.343332** |
Netherlands | 0.599342* | -0.219322** | -2.837685* | -0.337172 |
Norway | -0.230284** | 0.845168** | 0.659952 | 0.498667* |
Peru | 0.026872 | -0.364033* | 0.187332 | 0.158328* |
Poland | 0.181681 | -0.056451 | -0.586811 | -0.154949 |
Portugal | 0.091672 | -0.342057* | 0.103684 | -0.084911 |
Romania | 0.237314* | -0.288313* | -0.828651* | -0.155023 |
Saudi Arabia | 1.026827* | -0.062993 | -5.976091* | 0.292289** |
Slovenia | 0.128535 | -0.410748 | -0.513299 | 0.418659 |
South Africa | -0.382278* | -0.312063* | 2.558199* | 0.199467 |
Spain | 0.204402 | -0.609967* | -0.661970 | 0.072806 |
Sweden | 0.031784 | -0.071500 | 0.170439 | 0.175702*** |
Thailand | -0.559202 | 0.749077 | 2.265330 | 0.795743 |
Turkey | 0.192589* | -0.303971* | -0.642183** | -0.135102** |
Ukraine | 0.158640 | -0.185441* | -0.511889 | -0.130195 |
United Kingdom | -0.076811 | -0.047636 | 1.025801 | -0.206886 |
United States | -0.240143* | 0.114915 | 1.910811* | -0.048103 |
Note
*, ** and *** indicates one, five and ten percentage significant levels.