Assessing Long-run Dynamics of Financial Hedging, A New Determinants of Green Financing in E7 and G7 Countries


 The study estimates the long-run dynamics of a cleaner environment in promoting the gross domestic product of E7 and G7 countries. The recent study intends to estimate the climate change mitigation factor for a cleaner environment with the GDP of E7 countries and G7 countries from 2010 to 2018. For long-run estimation, second-generation panel data techniques including Augmented Dickey-Fuller (ADF), Phillip-Peron technique and fully modified ordinary least square (FMOLS) techniques are applied to draw the long-run inference. The results of study are robust with VECM technique. The outcomes of study revealed that climate change mitigation indicators affect more to the GDP of G7 countries than E7 countries. The GDP of both E7 and G7 countries is found depleting due to less clean environment. However, green financing techniques may clean the environment and reinforce the confidence of policymakers on the elevation of green economic growth in G7 and E7 countries. Furthermore, results show that a 1% rise in green financing index improves the environmental quality by 0.375% in G-7 countries, while it purifies 0.3920% environment in E7 countries. There is a need to reduce environmental pollution, shift energy generation sources towards alternative, innovative and green sources.


Introduction
emerging Asian economies is highlighted in this report, which adds to the current literature. 181 As seen in the discussion above, there is enough research on the relationship between energy 183 and development.  all agree with the literature review in Table 1. However,  184 there is a scarcity of evidence on the impact of energy demand on economic development in 185 emerging Asian economies in general. There is also no proper conclusion or findings in this data. 186 As a result, there is a pressing need to put the energy-growth nexus discussions to rest (Tang et al., 187 2018). Furthermore, there is yet to be released a report that explores the impact of renewables on 188 economic development incorporating both renewable and nonrenewable energy. As a 189 consequence, this research is important in bolstering the third strand of literature, which seeks to 190 fill this void in the literature for emerging Asian economies. 191  Russia, Indonesia and Turkey were taken. While, united states, United Kingdom, Germany, Japan, 204

Data and Methodology
France, Italy and Canada were taken in G7 countries. In total, 14 countries were taken which are 205 major countries facing issues in terms of environmental pollution and reduction in economic 206 growth. Subsequently, this is to assess the log-run dynamics of cleaner environment on economic 207 indicators. The cleaner environment is also assessed by using the green performance index data of 208 E7 countries and G7 countries. Table 1 of the study shows the green performance index scores of 209 E7 and G7 countries. Notably, the empirical statistics revealed that G7 countries are more attentive 210 to clean the environment for climate change mitigation, concerned to gain environmental 211 sustainability and this matters them most than E7 countries.

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In this study, we examine the impact of climate change on macroeconomic indicators of BRI 214 project and G7 AND E7 region. To acquire the study objectives, we consider two models (Y: 215 growth function and CE: environmental function), which are specified as follows: 216 where i designates countries; t represents the period; α0 represents the fixed country effect, 225 and ε is the white noise. Ln is the natural logarithms of all variables. Moreover, the logarithmic 226 form of equation (3) is developed as, 227 where, the country, t is the period, and εit is the error term. The parameters, such as, β1, β2, 229 and β3 represent the long-run elasticity estimates of Y, X, Pre-test exposure, and post-test exposure 230 of the countries, in G7 and E7 region, respectively. 231

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A panel stationary test is applied to test to assess the order of variable integration. For this, 233 For this and long-run inference of results we applied VECM methods by using two-step process. 252  on EE in the E7 and G7 nations. All of the covariates show that the BRI project countries in E7 265 and G7 countries with means have an effect on energy quality. To measure the long-run 266 relationship between the structures, the VECM procedure is used. The F-statistics in the VECM 267 may indicate short-run causality, whereas the error correction word ECT (1) may indicate long-268 run causality. Therefore, the equation of the VECM for economic growth (Y) is written as follows: 269 11  12  13  21  22  23  31 32 33 In above equation (5), three main dimensions were taken, such as, environmental, social and 271 economic to assess the cleaner environment, climate change and economic growth prospects in 272 BRI project and G7 and E7 regions. 11  21  12  22  13  23   31  41  32  42  33 43 The vector error (VECM) form of study model is written and sub-divided into proxies as 276 follows, where Δ, δit, γit, i, t, and μit represent the first difference operator, the constant term, the 277 parameters, the period and the error term, respectively. ECT is the lagged error correction term. 278 Using above econometric models, we used long run growth prospecting econometric function (see The results indicate that decreased fossil fuel usage and increased renewable energy 290 consumption caused development in the E7 and G7 regions. Backs up this point by citing 291 Indonesia's goal of producing 5% of its electricity from geothermal, 5% from wind, biomass, 292 hydro, and solar, and 5% from biofuel by 2025. In order to improve and achieve a low-carbon 293 economy, Indonesia initiated the Low Carbon Growth Initiative (LCDI). This aim also promotes 294 the creation of a policy suite and modular transformation programs that can be used in various 295 economic sectors. These revolutionary processes could result in economic growth of 5.6 percent 296 by 2020 and 6.0 percent by 2045. 297 In the best-case scenario, 15.3 million good green workers will be introduced by 2045, 298 resulting in a $5.4 trillion GDP boost. Poverty is projected to fall from 9.8% of the population in 299 2018 to 4.2 percent in 2019. About the same way, better air quality is projected to save 40,000 300 lives (Zeng et al., 2017). During the period 2005-2015, the Philippines expected to raise its 301 renewable energy by 100%. In the last six years, the Philippines' economy has expanded at a steady 302 pace of 6.6 percent. By 2030, it intends to build 2.35 GW of wind power. However, the theoretical 303 capacity is 76 GW (Baloch et al., 2020). With steady GDP growth of 6% over the last decade, 304 Vietnam can be called another booming economy. Its clean energy goals are 5% in 2020 and 11% 305 in 2050, respectively (Ma et al., 2019). The nation currently has 228 MW of installed wind power 306 and expects to build 800 MW by the end of 2020. The G-7 AND E-7 countries have a large energy 307 intensity ratio, which should be ample incentive for them to engage in energy production and 308 conservation. 309 Extending to it, con-integration test is applied to build more econometric clarity in study results. 315 These results are tabulated in table 4 by applying Pedroni panel co-integration test with seven  316 diverse statistics, in which, three are between and four are within magnitudes. Table 4 shown that  317 there is a significant cointegration among the variables therefore H1 is accepted. 318 We used the FMOLS methodology to calculate the long-term association between variables. 319 For the estimates in Table 5, see this article. It validates the growth theory, which maintains that 320 economic growth is generated by energy usage. As an economy grows, the energy use is often direct investment (FDI) has a high mean influence on overall direct investment (QPI). Table 6  335 predicts that the respective mean and standard deviation for the logit and probit models lie between 336 0 and 1 The formula would not limit the range of probabilities to 0-1 for the Logit model, which 337 means they will take on every possible logit value. An equivalent or even higher mean value for 338 Ei, an equal mean for G-7 and E-7 countries with respect to energy production. as said above, the 339 sensitivity and specificity models were accurate in their predictions. See Figure 19, where the 340 model has a sensitivity of 89.33 and a reported value of 92.42, but a negative accuracy of 58.93 341 The findings of this analysis indicate was considered to be right to be at 84.21% Although 84% of 342 the model has been estimated to be right, the majority of the assumptions are in error. It is shown 343 in is recorded at 0.049. This value is significant at 5% level. A GDP per capita based split analysis 357 on the whole sample is explained in this section. The two sub-divisions of the sample include the 358 countries with low GDP per capita and a high GDP per capita. The three non-parametric tests 359 applied include the rank-sum equality, equality of distribution and rank comparison. 360  A large rise in electricity consumption has been induced by the population as well. estimated 364 another input parameter estimated in the model, this time, the G-7 countries gave a response of 365 99.37% (Table 5)

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To estimate the long-run association among the study constructs, we applied FMOLS 376 technique. Our findings reported the growth function in the table 6. It seemed that cleaner 377 environment or in other words climate change mitigation in terms of CO2 emission reduction has 378 positive impacts on economic growth of BRI project and G7 and E7 region countries. Importantly, 379 renewable energy sources have significantly moderated in this relationship and inclined the role 380 towards positive extent. However, role of green financing in terms of renewable energy sources 381 usage has commendable role. All the countries of E7 and G7 region reported the relationship 382 between variables, as significant. This commends a significant role of green financing techniques 383 through renewable energy sources for environmental cleaning and greening. Such results validated 384 the growth hypothesis, suggesting a unidirectional causality relationship between environmental 385 cleaning and economic growth of G7 and E7 region and BRI project. This suggest more that using 386 innovative energy solutions for the energy consumption holds a vital role in regional economic 387 growth and climate change mitigation, directly and indirectly (Assadi et al., 2020). 388 Table 6 Long run estimates of the growth function 389  As a consequence, the panel findings remained relevant in two ways: first, construct-wise, 392 and second, country-wise. Since the residual errors are usually distributed, we can trust the findings 393 recorded by the models, which are 1% for the lower percentiles and 99% for the higher percentiles. 394 Floods endanger 48% of the world's property, more than half of the world's people, and 46% of 395 global properties. In 68% of coastal regions, tidal and storms will cause flooding, while the 396 remaining 32% is at risk from a regional increase in sea level, according to his report. The study also reveals the flow of green finance in G-7 and E-7 nations. The developing countries are host 398 to the bulk of the world's population. In 2018, the total and nominal GDP of the world's population 399 was projected to be about $6.5 trillion, with about 1.5 billion people. While having a population 400 that is larger than China, their GDP is comparable to China's. This level of magnitude revealed 401 that 0.34 represents a 1% increase in economic growth due to green energy demand, resulting in a 402 0.11 increase in economic growth from where it is now. As a consequence, our results are 403 compatible with previous research on E7 and G7 regional initiatives in multiple contexts, 404 highlighting the role of a cleaner environment in economic development by green finance on 405 regional scales such as the G7 and E7. We have used the effects of the environmental feature with 406 the growth function, as seen in table 7, utilizing the FMOLS technique. These findings indicate 407 that CO2 levels are elastic as green energy is used in combination with G7 economic development. 408

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Interestingly, there is slight difference of graphs between E7 and G7 countries but comparatively 410 G7 countries are more inclined to take initiatives for climate change mitigation. As Brazil holds 411 lower score ranging from 46% to 54% which is lowest score in E7 countries, as well as in G7 412 countries. Mexico has good index in terms of green performance which is greater than 75%. China 413 is setting a benchmark in green performance index achieving more than 93% score to perform 414 green. Indonesia is sluggish to perform as green countries holding score less than 60%, which is 415 quite alarming and indicating to take quick actions for a secure environmental future, nation-wide. 416 While, in G7 countries only France is less efficient to perform green and having score less than 417 60%. Conclusively, G7 has one country (e.g France) and E7 has two countries (e.g. Brazil and 418 Indonesia). 419  The aim of this analysis was to look at the impact of climate change mitigation on GDP in 442 the E7 and G7 nations, as well as other determinants including environmental taxation, human 443 resources, GDP, green energy use, and environmentally sustainable technical innovation. For a 444 variety of factors, we decided to analyses a sample of G-7 and E-7 nations. The strategy, strategies, 445 and activities of these seven great powers, which control nearly half of global GDP, are critical in 446 achieving low CO2 levels. G7 countries' attempts to curb CO2 pollution are commendable, given 447 that their exposure to greenhouse gas emissions was 70% in the early twentieth century and just 448 24% in 2015. Despite the fact that its absolute contribution to greenhouse gas emissions is high, 449 the G7's contribution is just half that of China as of early 2010. Canada has the largest greenhouse 450 gas emissions and electricity use per capita in the E7 nations. 451 As long as it proceeds to subsidies the use and output of fossil fuels, Canada's success in 452 climate change mitigation policy is rated as average. Furthermore, the United Kingdom, Indonesia, and Germany have excellent results in terms of greenhouse gas emissions and oil usage, while the economic growth. Therefore, BRI economies are recommended to formulate country-specific 522 strategies for better benefit. 523 524 Policy Implications 525 The following are the policy recommendations in this article: 526 To begin, increase the severity of environmental regulations as required. The environmental 527 regulation has had the anticipated "back-forced reduction" impact at this stage. As a result, 528 improving environmental policies would aid in the reduction of carbon emissions. 529 Second, the government should set the level of environmental regulation based on regional 530 economic growth and carbon intensity heterogeneity. It is recommended that the eastern developed 531 provinces adopt a higher degree of environmental regulation severity, taking into account the 532 growing demand for environmental quality and green goods. 533 Third, technical innovation should have a positive impact on carbon emissions reduction. The 534 empirical findings indicate that technological progress has not substantially decreased carbon 535 emissions under the constraints of environmental regulation. As a result, the government should 536 build an external climate conducive to corporate environmental protection technology innovation, 537 as well as direct the transition of innovation inputs to environmental protection technologies, based 538 on local conditions. Enterprises should aggressively adopt environmental management 539 technologies that are compatible with their own productivity levels and technical absorption 540 capability, undertake reverse learning and secondary growth, fully exploit the advantages of late 541 development, and realise the dynamic evolution from technology import to technology imitation 542 to independent innovation. 543

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The authors declare that they have no Known competing financial interests or personal 545 relationships that seem to affect the work reported in this article. We declare that we have no 546 human participants, human data or human tissues. 547     Proposed policy frameworks