The Asymmetric and Long-Run Effect of Financial Stability on Environmental Degradation in Norway
Abstract
:1. Introduction
2. Methodology
3. Empirical Finding and Discussion
4. Conclusions and Policy Direction
- Norway is in a prime position to lead the way toward reducing its reliance on fossil fuels. Having relatively good economic standing, Norway may be in a better position than many other countries to switch to renewable energy from fossil fuel production because it is one of the wealthiest nations globally. Policymakers in Norway should first examine in detail the effects of global emissions on the economy under a wide range of scenarios for future climate policy so that they can make informed decisions about national petroleum policy in the long run. Further, there should be a robust energy policy in Norway. A climate test should be used by Norway in order to ensure that planned oil production is consistent with climate goals in order to bridge the climate and energy policy gap.
- The financial sector should step up to support and lead the transition to a low-carbon economy. A lot of attention is being paid to evolving approaches, technologies, and methods that can be used to quantify how much lending and investment activities contribute to emissions. Using this study’s findings, it is recommended that Norway concentrates on creating a stable financial system, which will encourage companies to adopt more advanced and efficient technologies, and in turn, will help to decrease energy consumption and improve the environment. Policymakers should encourage firms to adopt eco-friendly technologies and give them incentives to improve environmental quality through the financial sector. Obtaining a financial benefit will motivate firms to adopt environmentally friendly technologies, resulting in a reduction of energy consumption and carbon emissions.
- Moreover, regulations should be put in place that limit the availability of loans for businesses that discharge more waste into the water and air. Furthermore, in order to minimize carbon emissions caused by economic growth, Norway should pay attention to domestic consumers and energy-intensive industries. The switch to green energy is vital for rapid and cost-effective climate action. Since Norway has a robust economy, it should invest in renewable energy technologies with low emissions and impact on the environment, such as wind, solar, and hydro.
- To reduce emissions from the transport sector, key policies including carbon taxation, biofuel quotas, and requirements for using zero and low-emission technologies, as well as investment support schemes, must be strengthened.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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CO2E | FRI | EGR | RENN | TRO | |
---|---|---|---|---|---|
Mean | 1.639571 | 3.817371 | 11.52861 | 1.823798 | 11.26344 |
Median | 1.644849 | 3.828641 | 11.54582 | 1.825861 | 11.27240 |
Maximum | 1.661848 | 3.888413 | 11.60989 | 1.861317 | 11.34623 |
Minimum | 1.566293 | 3.569533 | 11.39621 | 1.789666 | 11.10469 |
Std. Dev. | 0.016813 | 0.056395 | 0.056913 | 0.014033 | 0.052989 |
Skewness | −1.533885 | −2.180503 | −0.506929 | −0.188092 | −0.853525 |
Kurtosis | 6.688577 | 9.357554 | 2.315771 | 3.169775 | 3.555202 |
Jarque-Bera | 95.90339 | 247.6536 | 6.233655 | 0.709744 | 13.42612 |
Probability | 0.000000 | 0.000000 | 0.044297 | 0.701263 | 0.001215 |
CO2E | ||||
---|---|---|---|---|
Dimension | BDS Statistic | Std. Error | z-Statistic | Prob. |
2 | 0.168316 | 0.007737 | 21.75403 | 0.0000 |
3 | 0.283795 | 0.012368 | 22.94651 | 0.0000 |
4 | 0.359338 | 0.014812 | 24.26011 | 0.0000 |
5 | 0.406884 | 0.015526 | 26.20583 | 0.0000 |
6 | 0.438087 | 0.015059 | 29.09110 | 0.0000 |
FRI | ||||
Dimension | BDS Statistic | Std. Error | z-Statistic | Prob. |
2 | 0.124591 | 0.009201 | 13.54141 | 0.0000 |
3 | 0.200905 | 0.014712 | 13.65612 | 0.0000 |
4 | 0.251135 | 0.017629 | 14.24556 | 0.0000 |
5 | 0.276599 | 0.018492 | 14.95787 | 0.0000 |
6 | 0.283626 | 0.017949 | 15.80216 | 0.0000 |
EGR | ||||
Dimension | BDS Statistic | Std. Error | z-Statistic | Prob. |
2 | 0.206274 | 0.005371 | 38.40511 | 0.0000 |
3 | 0.350489 | 0.008575 | 40.87364 | 0.0000 |
4 | 0.451995 | 0.010253 | 44.08257 | 0.0000 |
5 | 0.524054 | 0.010729 | 48.84303 | 0.0000 |
6 | 0.575553 | 0.010387 | 55.40981 | 0.0000 |
RE | ||||
Dimension | BDS Statistic | Std. Error | z-Statistic | Prob. |
2 | 0.143721 | 0.007774 | 18.48745 | 0.0000 |
3 | 0.226231 | 0.012448 | 18.17460 | 0.0000 |
4 | 0.269320 | 0.014933 | 18.03506 | 0.0000 |
5 | 0.285611 | 0.015680 | 18.21456 | 0.0000 |
6 | 0.285535 | 0.015234 | 18.74269 | 0.0000 |
TR | ||||
Dimension | BDS Statistic | Std. Error | z-Statistic | Prob. |
2 | 0.201897 | 0.007630 | 26.45935 | 0.0000 |
3 | 0.343007 | 0.012182 | 28.15675 | 0.0000 |
4 | 0.442892 | 0.014572 | 30.39429 | 0.0000 |
5 | 0.513316 | 0.015256 | 33.64775 | 0.0000 |
6 | 0.563869 | 0.014778 | 38.15610 | 0.0000 |
At Level | ||||||
---|---|---|---|---|---|---|
CO2E | FRI | RENN | TRO | EGR | ||
LS | t-Statistic (tau) | −4.1304 | −5.4503 | −4.8015 | −5.2641 | −4.071412 |
Break Points | 2000Q4 2008Q4 | 2008Q4 2015Q3 | 1999Q3 2004Q3 | 1998Q3 2008Q4 | 2004Q1 2009Q3 | |
Test critical values | 1% level | −6.9320 | −6.932000 | −6.7500 | −6.9320 | −6.821000 |
5% level | −6.1750 | −6.175000 | −6.1080 | −6.1750 | −6.166000 | |
10% level | −5.8250 | −5.825000 | −5.7790 | −5.8250 | −5.832000 | |
At First Difference | ||||||
CO2E | FRI | RENN | TRO | EGR | ||
LS | t-Statistic (tau) | −6.7717 *** | −10.839 *** | −6.2995 ** | −7.1696 *** | −6.5129 ** |
Break Points | 1997Q3 2016Q3 | 2000Q1 2015Q1 | 1999Q3 2003Q3 | 2002Q3 2007Q3 | 2003Q3 2011Q1 | |
1% level | −6.8210 | −6.8210 | −6.7500 | −6.9320 | −6.9780 | |
5% level | −5.9170 | −5.9170 | −6.1080 | −6.1750 | −6.2880 | |
10% level | −5.5410 | −5.5410 | −5.7790 | −5.8250 | −5.9980 |
Fourier ADL Cointegration Analysis | ||||
---|---|---|---|---|
Model | Test Statistics | Frequency | Min AIC | |
CO2E = f(FRI, EGR, RE, TRO) | −5.772 *** | 2 | −8.661645 | |
Nonlinear ARDL Bounds Test | ||||
F-Bounds Test | Value | Signif. | I (0) | I (1) |
F-statistic | 8.104871 *** | 10% | 1.85 | 2.85 |
K | 8 | 5% | 2.11 | 3.15 |
2.5% | 2.33 | 3.42 | ||
1% | 2.62 | 3.77 |
Variable | Coefficient | Std. Error | t-Statistic | Prob. | |||
FRI_POS | −0.306617 *** | 0.081949 | −3.741570 | 0.0004 | |||
FRI_NEG | −0.063510 * | 0.036574 | −1.736482 | 0.0869 | |||
EGR_POS | 2.829711 *** | 0.902205 | 3.136439 | 0.0025 | |||
EGR_NEG | 16.58350 ** | 7.415659 | 2.236281 | 0.0286 | |||
RENN_POS | −1.178169 *** | 0.361015 | −3.263494 | 0.0017 | |||
RENN_NEG | −0.336383 ** | 0.142563 | −2.359542 | 0.0211 | |||
TRO_POS | −0.879127 * | 0.453962 | −1.936565 | 0.0569 | |||
TRO_NEG | −5.043676 ** | 2.203409 | −2.289033 | 0.0251 | |||
C | 1.568700 *** | 0.016248 | 96.54816 | 0.0000 | |||
CointEq (−1) * | −0.200395 *** | 0.020936 | −9.571848 | 0.0000 | |||
Diagnostic Test | |||||||
Heteroskedasticity Test: Breusch-Pagan-Godfrey | |||||||
F-statistic | 1.289038 | Prob. F(25,69) | 0.2031 | ||||
Breusch-Godfrey Serial Correlation LM Test: | |||||||
F-statistic | 0.980167 | Prob. F(2,67) | 0.3806 |
Long Term | Medium Term | Short Term | ||||
---|---|---|---|---|---|---|
Direction of causality | ωi = 0.01 | ωi = 0.05 | ωi = 1.00 | ωi = 1.50 | ωi = 2.00 | ωi = 2.50 |
FRI → CO2E | < 8.719 > ** (0.012) | < 8.650 > ** (0.013) | < 0.226 > (0.893) | < 4.954 > * (0.084) | < 0.330 > (0.847) | < 1.735 > (0.419) |
RENN → CO2E | <1.391> (0.498) | <1.352> (0.508) | <3.089> ** (0.213) | <8.871> ** (0.011) | <7.909> ** (0.019) | <10.699> *** (0.004) |
EGR → CO2E | <6.936> ** (0.031) | <6.785> ** (0.033) | <2.792> (0.247) | <4.837> * (0.089) | <3.851> (0.145) | <2.050> (0.358) |
TRO → CO2E | <7.241> ** (0.026) | <7.488> ** (0.023) | <6.699> ** (0.035) | <6.333> ** (0.042) | <4.234> (0.120) | <6.063> ** (0.048) |
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Kirikkaleli, D.; Castanho, R.A.; Genc, S.Y.; Oyebanji, M.O.; Couto, G. The Asymmetric and Long-Run Effect of Financial Stability on Environmental Degradation in Norway. Sustainability 2022, 14, 10131. https://doi.org/10.3390/su141610131
Kirikkaleli D, Castanho RA, Genc SY, Oyebanji MO, Couto G. The Asymmetric and Long-Run Effect of Financial Stability on Environmental Degradation in Norway. Sustainability. 2022; 14(16):10131. https://doi.org/10.3390/su141610131
Chicago/Turabian StyleKirikkaleli, Dervis, Rui Alexandre Castanho, Sema Yilmaz Genc, Modupe Oluyemisi Oyebanji, and Gualter Couto. 2022. "The Asymmetric and Long-Run Effect of Financial Stability on Environmental Degradation in Norway" Sustainability 14, no. 16: 10131. https://doi.org/10.3390/su141610131
APA StyleKirikkaleli, D., Castanho, R. A., Genc, S. Y., Oyebanji, M. O., & Couto, G. (2022). The Asymmetric and Long-Run Effect of Financial Stability on Environmental Degradation in Norway. Sustainability, 14(16), 10131. https://doi.org/10.3390/su141610131