Evaluating Renewable Energy’s Role in Mitigating CO2 Emissions: A Case Study of Solar Power in Finland Using the ARDL Approach
Abstract
:1. Introduction
2. The Stage of Knowledge in the Field
2.1. Literature Review of Qualitative Studies on Renewable Energy
2.2. Literature Review of Quantitative Studies on Renewable Energy
3. Methodology and Data Collection
4. Empirical Results
4.1. Qualitative Analysis: PESTLE Framework
4.2. Quantitative Analysis: ARDL Approach
4.2.1. Diagnostic and Model Stability Assessment
4.2.2. Robustness Checks and Granger Causality Analysis
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Acronym | Definition |
---|---|
SWOT | Strengths, Weaknesses, Opportunity, and Threats |
PESTLE | Political, Economic, Social, Technological, Legal, Environmental |
ARDL | Autoregressive Distributed Lag |
FDI | Foreign Direct Investments |
GDP | Gross Domestic Product |
SOL | Share of primary energy consumption from solar |
URB | Urbanization |
ECM | Error Correction Model |
ECT | Error Correction Term |
VAR | Vector Autoregression |
FPE | Final Prediction Error |
AIC | Akaike Information Criterion |
SC | Schwarz Information Criterion |
HQ | Hannan–Quinn Information Criterion |
PP | Phillips–Perron |
FMOL | Fully Modified Ordinary Least Squares |
DOLS | Dynamic Ordinary Least Squares |
CCR | Canonical Cointegrating Regression |
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First Author, Year, Ref. | Objective | Methods | Main Findings |
---|---|---|---|
Guangul, 2019, [25] | Assessment of the advantages, disadvantages, possibilities, and threats related to solar energy utilization. | SWOT Analysis |
|
Lupu, 2016, [26] | The authors assess Romania’s renewable energy sector’s potential, present situation, and future perspective, with a particular emphasis on solar energy. | SWOT Analysis |
|
Pihkola, 2017, [27] | Sustainability analysis of carbon capture and storage (CCS) technologies from a multidisciplinary perspective. Also, another objective of this study is to identify the main factors and barriers influencing the implementation of CCS technologies in Finland and the impact on the general environment. | PESTLE Analysis |
|
Shahsavari, 2018, [16] | Benefits of solar energy utilization and barriers to widespread adoption. | Literature review | Identifies technological and economic barriers to solar energy adoption |
Salam, 2018, [28] | Factors driving Saudi Arabia’s shift towards solar energy. | Case study—qualitative analysis | Photovoltaic systems are an effective way to provide basic energy services. |
Kumar, 2023, [31] | Evaluation of the actual status, importance, availability, and applications of solar technologies in Indian agriculture. The study also analyzes the socioeconomic and environmental impact, economic benefits, strengths, weaknesses, and future technological potential of solar energy in agriculture. | SWOT analysis; Economic analysis; Literature review; Environmental Impact Assessment. |
|
Mohtaram, 2024, [32] | The study explores biochar-based photocatalytic methods for CO2 conversion and hydrogen (H2) production in solar fuel generation, highlighting the importance of improving the efficiency and stability of these photocatalysts. | Evaluation of current approaches in CO2 conversion and associated strategies, including the use of solar concentrators and thermal methods to improve the generation of solar fuels by artificial photosynthesis. |
|
Ahmed, 2024, [33] | The study assesses current electricity consumption in households in the Gulf Cooperation Council (GCC) and proposes three scenarios for reducing energy consumption and CO2 emissions. | SWOT analysis. PESTLE analysis. |
|
Thompson, 2023, [34] | In order to slow down climate change, the study investigates the role that electricity plays in the switch to renewable energy sources. | Systematic analysis of literature review. PESTLE analysis. |
|
First Author, Year, Ref. | Objective | Methods | Main Findings |
---|---|---|---|
Al-Janabi, 2023, [45] | Evaluation and development of a software model to maximize solar energy production using advanced prediction techniques and multi-parameter objective functions. | Deep learning method (DMP-DGBM). |
|
Soto, 2024, [46] | Evaluating the carbon reduction impact of solar energy integration into specific U.S. electric grids. | Scenario analysis |
|
Kinnunen, 2024, [47] | Evaluating the Environmental Phillips Curve Hypothesis in Finland’s STIRPAT 1990–2022 to understand the balance between economic growth and environmental impact. | Autoregressive distributed lag (ARDL) |
|
Güney, 2024, [38] | The study examines the long-term relationships between solar power, globalization, coal power consumption, economic growth, and CO2 emissions in 26 countries over the period 2000–2019. | The method of correlated effects on mean groups (CCEMG); The OLS, FMOLS, and CCEMG estimates. |
|
Perone, 2024, [48] | The study examines the long-term relationship between decoupled renewable energy production and carbon dioxide (CO2) emissions per capita for a group of 27 OECD countries over the period 1965–2020. | Panel-autoregressive distributed lag (ARDL) models. |
|
Variables | Acronym | Measurement Unit | Source |
---|---|---|---|
emissions per capita | tonnes | Our World in Data | |
Foreign direct investments, net inflows | FDI | % of GDP | Our World in Data |
Gross domestic product | GDP | constant 2015 $USD | Our World in Data |
Share of primary energy consumption from solar | SOL | % | Our World in Data |
Urbanization | URB | % | Our World in Data |
Political | Economic | Social | Technological | Legal | Environment |
---|---|---|---|---|---|
Government support: Finland has strong policies to support renewable energy, including subsidies and incentives for solar installations. | Upfront costs: Installing solar panels involves high upfront costs, but long-term costs are reduced due to low maintenance and savings on energy bills. | Public awareness: Increasing awareness among the population about the benefits of renewable energy and the impact on the environment. | Innovation and development: Technological progress in the efficiency of solar panels and the development of new materials to increase efficiency and durability. | Favorable legislation: Laws that support the installation of solar panels and provide tax breaks for the adoption of renewable energy. | Emission reduction: Solar energy contributes significantly to the reduction of CO2 emissions, supporting efforts to combat climate change. |
International agreements: Joining the Paris Agreement and other international commitments requiring a clear target for reducing CO2 emissions. | Job creation: The development of solar energy creates employment opportunities in the solar system installation and maintenance sector. | Social acceptance: Community support and acceptance of solar technologies as viable energy solutions. | Integration with existing grids: Need for infrastructure development to effectively integrate solar energy into existing power grids. | Norms and standards: Clear regulations on the quality and safety of solar installations to ensure compliance and optimal performance. | Protection of natural resources: The use of solar energy reduces the pressure on natural resources and contributes to the preservation of the environment. |
Regulations: Strict regulatory policies on CO2 emissions and promoting the use of green energy to meet climate target. | Energy independence: Increasing the use of solar energy can reduce dependence on fossil fuel imports, stabilizing the economy in the long term. | Education and training: The need for educational programs to train solar energy specialists and promote widespread adoption. | Storage solutions: Advances in energy storage technologies are essential to manage the intermittency of solar power and ensure a steady flow of power. | Contractual agreements: The need for strong contracts for the supply of solar energy and ensuring a legal framework for investment. | Impact on biodiversity: Solar installations must be carefully planned to minimize impact on natural habitats and biodiversity. |
GDP | SOL | URB | FDI | ||
---|---|---|---|---|---|
Mean | 2.35 | 10.56 | 5.70 | 4.41 | 1.08 |
Median | 2.43 | 10.63 | 6.35 | 4.41 | 1.55 |
Maximum | 2.63 | 10.75 | 1.14 | 4.45 | 2.37 |
Minimum | 1.87 | 10.23 | 7.78 | 4.37 | 1.31 |
Std. Dev. | 0.20 | 0.17 | 1.83 | 0.02 | 1.05 |
Skewness | 0.92 | 0.66 | 1.28 | 0.03 | 0.86 |
Kurtosis | 2.73 | 1.99 | 3.67 | 1.94 | 2.65 |
Jarque–Bera | 3.90 | 3.14 | 7.95 | 1.25 | 3.53 |
Probability | 0.14 | 0.20 | 0.01 | 0.53 | 0.17 |
Variable | Level | First Difference | Order of Integration |
---|---|---|---|
T-Statistics | T-Statistics | ||
0.76 (0.95) | 73.45 *** (0.00) | I (1) | |
GDP | 0.99 (0.93) | 4.22 ** (0.01) | I (1) |
SOL | 0.40 (0.98) | 7.89 *** (0.00) | I (1) |
URB | 2.42 (0.35) | 3.69 ** (0.03) | I (1) |
FDI | 2.83 (0.20) | 9.32 *** (0.00) | I (1) |
Variables | Level | First Difference | Order of Integration | ||
---|---|---|---|---|---|
T-Statistics | Break Year | T-Statistics | Break Year | ||
−2.58 (0.87) | 2011 | −8.15 *** (0.00) | 2003 | I (1) | |
GDP | −3.79 (0.23) | 1996 | −7.11 *** (0.00) | 2008 | I (1) |
SOL | −2.90 (0.73) | 2014 | −6.59 *** (0.00) | 2015 | I (1) |
URB | −8.73 *** (0.00) | 2009 | −4.31 * (0.07) | 2011 | I (0) |
FDI | −7.11 *** (0.00) | 1997 | −11.81 *** (0.00) | 1998 | I (0) |
Lag | LogL | LR | FPE | AIC | SC | HQ |
---|---|---|---|---|---|---|
0 | 77.14 | N/A | 8.48 | 8.24 | 8.46 | |
1 | 228.90 | 196.40 * | * | 21.53 * | 23.25 | |
2 | 261.98 | 23.34 | 24.35 * | 21.65 | 24.08 * |
Tests | Engel–Granger EG | Johansen J | Banerjee BA | Boswijk BO |
---|---|---|---|---|
Test statistic | 4.02 | 43.35 | 4.16 | 26.97 |
p-value | 0.11 | 0.00 | 0.03 | 0.00 |
EG-J | 16.02 | 5% critical value, 10.57 | ||
EG-J-BA-BO | 35.55 | 5% critical value, 20.14 |
Tests | Value | K (Number of Regressors) |
---|---|---|
F-statistic | 4.92 | 4 |
Critical value bounds | ||
Significance | I (0) | I (1) |
10% | 2.20 | 3.09 |
5% | 2.56 | 3.49 |
1% | 3.29 | 4.37 |
Variables | Coefficient | T-Statistics | Prob. |
---|---|---|---|
GDP | 0.99 | 3.88 | 0.00 *** |
SOL | 0.09 | 4.79 | 0.00 *** |
URB | 5.01 | 1.76 | 0.09 * |
FDI | 0.07 | 2.18 | 0.04 ** |
C | 13.58 | 1.25 | 0.22 |
Variables | Coefficient | T-Statistics | Prob. |
---|---|---|---|
D(GDP) | 2.61 | 4.86 | 0.00 *** |
D(SOL) | 0.20 | 4.21 | 0.00 *** |
D(URB) | 14.85 | 2.69 | 0.01 *** |
CointEq (−1) | 0.83 | 6.18 | 0.00 *** |
R-squared | 0.66 | ||
Adjusted R-squared | 0.61 |
Diagnostic Test | Decision Statistics [p-Value] | |
---|---|---|
Serial Correlation | There is no serial correlation in the residuals | Accept 0.87 [0.43] |
Heteroscedasticity (ARCH) | There is no autoregressive conditional heteroscedasticity | Accept 0.01 [0.90] |
Jarque–Bera | Normal distribution | Accept 1.15 [0.56] |
Ramsey RESET | Absence of model misspecification | Accept 0.38 [0.70] |
Variables | FMOLS Coefficient, (t-Statistics), [p-Value] | DOLS Coefficient, (t-Statistics), [p-Value] | CCR Coefficient, (t-Statistics), [p-Value] |
---|---|---|---|
GDP | 1.02 | 0.88 | 1.00 |
(4.72) | (3.34) | (4.55) | |
[0.00] *** | [0.00] *** | [0.00] *** | |
SOL | −0.09 | −0.10 | −0.09 |
(−3.81) | (−4.69) | (−4.28) | |
[0.00] *** | [0.00] *** | [0.00] *** | |
URB | −6.44 | −4.89 | −6.69 |
(−2.26) | (−1.92) | (−2.94) | |
[0.03] ** | [0.06] * | [0.00] *** | |
FDI | −0.03 | −0.01 | −0.03 |
(−1.63) | (−0.58) | (−1.12) | |
[0.12] | [0.56] | [0.22] | |
C | 2.03 | 14.06 | 20.14 |
(1.74) | (1.43) | (2.27) | |
[0.10] | [0.16] | [0.03] ** |
Null Hypothesis | F-Statistic | Prob. | Conclusion |
---|---|---|---|
GDP d.n.G.c. CO2 | 2.19 | 0.13 | |
CO2 d.n.G.c. GDP | 1.77 | 0.18 | |
SOL d.n.G.c. CO2 | 3.18 | 0.05 * | |
CO2 d.n.G.c. SOL | 1.60 | 0.22 | |
URB d.n.G.c. CO2 | 3.03 | 0.06 * | |
CO2 d.n.G.c. URB | 0.52 | 0.59 | |
FDI d.n.G.c. CO2 | 0.16 | 0.85 | |
CO2 d.n.G.c. FDI | 0.56 | 0.57 | |
SOL d.n.G.c. GDP | 0.24 | 0.78 | |
GDP d.n.G.c. SOL | 0.25 | 0.77 | |
URB d.n.G.c. GDP | 1.91 | 0.16 | |
GDP d.n.G.c. URB | 0.46 | 0.63 | |
FDI d.n.G.c. GDP | 0.67 | 0.51 | |
GDP d.n.G.c. FDI | 1.44 | 0.25 | |
URB d.n.G.c. SOL | 3.31 | 0.05 * | |
SOL d.n.G.c. URB | 0.27 | 0.76 | |
FDI d.n.G.c. SOL | 0.42 | 0.65 | |
SOL d.n.G.c. FDI | 0.15 | 0.85 | |
FDI d.n.G.c. URB | 0.73 | 0.48 | |
URB d.n.G.c. FDI | 1.23 | 0.30 |
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Nica, I.; Georgescu, I.; Kinnunen, J. Evaluating Renewable Energy’s Role in Mitigating CO2 Emissions: A Case Study of Solar Power in Finland Using the ARDL Approach. Energies 2024, 17, 4152. https://doi.org/10.3390/en17164152
Nica I, Georgescu I, Kinnunen J. Evaluating Renewable Energy’s Role in Mitigating CO2 Emissions: A Case Study of Solar Power in Finland Using the ARDL Approach. Energies. 2024; 17(16):4152. https://doi.org/10.3390/en17164152
Chicago/Turabian StyleNica, Ionuț, Irina Georgescu, and Jani Kinnunen. 2024. "Evaluating Renewable Energy’s Role in Mitigating CO2 Emissions: A Case Study of Solar Power in Finland Using the ARDL Approach" Energies 17, no. 16: 4152. https://doi.org/10.3390/en17164152