Determinants of Renewable Energy Production: Do Intellectual Property Rights Matter?

Abstract

Climate change and finite energy supply issues have received substantial public attention in recent times. It has been argued that a sustainable energy supply associated with the promotion of clean energy is an important engine of growth, which calls for sound protection to reinforce investments in the renewable energy market. This paper examined the effect of intellectual property rights (IPRs) on renewable energy production using the dynamic panel generalised method of moments (GMM) technique on data from 59 sample countries. The empirical results provided strong evidence that IPRs significantly drive renewable energy production. Greater protection rights motivate renewable energy firms to increase energy production from renewable resources. Our findings further revealed that stronger protection propagates the deployment of renewable energy technologies that ultimately promote renewable energy production.

1. Introduction

Renewable energy is an important tool to combat global warming; as such, it has been widely argued that the rapid growth of the renewable energy sector can directly reduce emission levels. Figure 1 indicates the energy supplies generated by renewable sources, revealing a gradual upward trend in the generation of world electricity by renewable energy since 1980. The total electricity produced by renewable energy achieved approximately 6000 billion Kilowatts in 2017. Globally, Asia and Oceania are the regions that have made the greatest contribution to overall electricity production from renewable energy at around 2500 kilowatt hours of electricity being generated by renewable resources. The upward trend implies that many countries have started to engage in energy transformation plans.

Despite the various advantages of renewable energy over non-renewable energy, energy consumption from renewable energy remains low. According to British Petroleum’s (BP) report, renewable energy accounted for only about four percent of global primary energy consumption in 2018. Oil remains the largest contributor, followed by coal and natural gas. Hydroelectricity, nuclear, and renewable energy contributed less than 10% in 2018 [2]. Reports widely concur that the greatest challenge facing renewable energy is a lack of adequate financing support [3]. For instance, Ref. [4] argues that the private sector only plays a marginal role in this industry, possibly due to low anticipated returns from renewable energy. However, the view of finance as the core mechanism to support the development of renewable energy is contrary to the finding of [5], who concluded that instead of financial aids, the primary factor that prohibits the growth of the renewable energy industry is the lack of supportive governmental policies for the alternative energy industry. We argue that most renewable energy projects require high projected up-front costs, competitive technologies, and longer payback times for return on investment. Therefore, the role of the government in intellectual property right (IPR) enforcement is highly important in facilitating the development of renewable energy. Strengthening IPRs enhance support for the renewable energy industry because investors’ creativity and creations will be protected by this governance framework. IPR protection also limits the imitation level of technology and eliminates the ‘free rider’ issue. This is crucial for investors in the renewable energy industry because renewable energy projects take significant amounts of time to repay their huge investment costs. A patent right is one example of an IPR. Figure 2 provides an overview of the renewable energy patents published by IRENA, which exhibits an upward trend since 2005. The substantial growth in patent applications in respective technology fields is likely a response to market conditions, including higher research and development (R&D) investment, shifts in policy incentives such as feed-in-tariffs, and technological advances such as cost reductions in manufacturing [6]. However, it is noteworthy that the upward trend, though increasing, is increasing at a diminishing rate. This raises the question of whether the slowdown in renewable energy patents poses harm to the development of renewable energy.

Scholars such as [7] state that developing a strong renewable energy sector in less developed countries can be accomplished by acquiring and adapting renewable energy technologies from developed nations. Arguably, strong and reliable IPR protection will encourage private investments from substantial fund holders, which in turn fosters research and innovation. Conversely, a lack of IPR governance will discourage private investment, as developers may not be willing to share knowledge with others, thereby reducing investment funds. IPR is thus decisive in determining participation in technology deployment, as it draws funding that encourages the creation and innovation of new technologies. IPR is likely to be an essential instrument to stimulate investments in renewable energy. However, it may create a barrier to the development of renewable energy if the cost of acquiring IPR is deemed too high. Higher investment costs in technology-related projects may be affordable for certain big investors, but not for a majority of them. Hence, the enforcement of IPRs ostensibly denotes a protected source of knowledge while creating a barrier to access [8].

Figure 2. Renewable energy patent evolution. Source: IRENA INSPIRE [9], based on data from EPO PATSTAT and the climate change technologies classification of EPO.

Figure 2. Renewable energy patent evolution. Source: IRENA INSPIRE [9], based on data from EPO PATSTAT and the climate change technologies classification of EPO.

Developing countries assert that intellectual property regimes prevent them from gaining access to critical technologies. It is good to have such protection as a reward scheme to recognise the contribution of inventors and encourage them to engage in similar transition plans. Without the protection of IPRs, inventors may have to rely on secrecy to avoid their inventions from being disclosed. Nevertheless, strictly adhering to IPR protection for the sake of monetary benefits will probably limit the diffusion of renewable energy. Consequently, technology transfer will be delayed and impede renewable energy technology adoption. Complying with IPRs should be achieved not because of financial incentives, but because technology sharing plays crucial role in fostering economic growth. Overall, there is uncertainty about whether the enforcement of IPRs prohibits or facilitates the development of renewable energy. However, empirical research on IPRs and renewable energy is relatively limited. Hence, this study aimed to examine whether IPRs present or do not present a barrier to renewable energy development.

This paper intended to contribute to the literature in at least two ways. First, research on IPRs and renewable energy is considerably scarce. To date, only two studies have discussed the link between IPR and renewable energy [7,10]. This study differs from these former studies by focusing on the production, rather than the consumption, of renewable energy. Second, most previous studies employed total patents as the proxy for IPR, which may not accurately depict the implications of IPR for renewable energy as patent data includes patents from all industries. Therefore, this study employed specific patent data (patents granted to environmental technologies and patents granted to climate change mitigation technologies) as the measurement for IPR since these patents are directly linked to renewable energy.

2. Literature Review

Substantial empirical works have underscored the relationship between IPR and various economic activities. For instance, Refs. [11,12,13] found that IPR is a significant engine of economic growth. Ref. [14] also concluded that the level of IPR protection determines the level of economic growth and technology transfer in low-income nations, while it has negative effects on both these outcomes in middle-income nations. These findings imply that innovation in developing countries may be generated through imitation, wherein stronger IPR protection favours foreign firms; therefore, incentives are necessary to boost local innovation in developing nations. Despite numerous preceding works establishing that IPRs have a straightforward effect on productivity and growth, Ref. [15] disputed these findings by concluding that IPRs do not spur productivity growth directly but indirectly through R&D investment. It has conclusively been shown that intellectual property is a valuable tool to promote the development and diffusion of green technology [16]. For example, Ref. [17] highlighted that intensive patent protection aids the profitability of green projects and subsequently leads to higher green energy-related private investment as well as new job creation associated with the acceleration of green energy technology progress.

Moreover, IPR protection acts as a shelter for inventions; in this manner, it provides a platform for knowledge transfer. This is especially true in the energy industry in which huge investment on technology is required. Ref. [18] demonstrated that weak intellectual property regimes are barriers to the deployment of low carbon technologies, as weak IPRs increase the time taken by firms to realise investment returns on innovation. Ref. [19] examined the potential factors such as energy security and level of fossil fuel production that affect renewable energy technology deployment, particularly patenting activity. Their results revealed that patenting activity was found to have a significant impact on renewable energy deployment in 26 OECD countries, while deployment of low carbon energy resources and national energy-related policies are ineffective. This contradicts similar work performed by [20], who presented evidence of the importance of public policy in encouraging the innovation of renewable energy technologies. IPR systems perform a significant function in ensuring that the efforts of talented individuals are not discriminated. In addition, IPRs allow for a certain amount of monopolistic use of new technology and knowledge by restraining use by third parties and competitors. Consequently, they decrease social benefits by prohibiting the application of patent rights for technology and knowledge.

Ref. [7] concluded that there is evidence showing that concerns toward IPR will not necessarily restrict the renewable energy transfer in China. However, a recent study conducted by [10] found that IPR protection and its reform do not significantly influence renewable energy consumption. This suggests that IPR is not a concern for global energy transition, which means policymakers should channel more efforts into economic factors that have stronger impacts on renewable energy adoption. Similarly, Ref. [21] found that IPR policies in the renewable energy sector are disconnected from reality, primarily due to the lack of rapid improvement in the sector’s innovation abilities.

As mentioned earlier, the research on IPRs and renewable energy is considerably scarce. To date, only two studies have discussed the link between IPR and renewable energy [7,10]. Ref. [10] researched on IPR, focusing on the role of IPR in renewable energy consumption rather than renewable energy production, while Ref. [7] discussed the link between IPR and renewable energy technology without incorporating regression analyses. One of the advantages of regression analysis is it allow for the examination of statistical relationship analysis, which subsequently can be treated as the references for policy recommendation. Therefore, our study contributes to the literature by being among the first to examine the impact of IPR on renewable energy production rather than consumption. Looking at the impact of IPR on renewable energy production allows policymakers to judge the effectiveness of renewable energy policies from both users’ and producers’ perspectives. The findings may thus encourage more renewable energy generation, particularly in countries that have just begun their energy transformation journey.

3. Data and Methodology

3.1. Model Specification

Studies on the determinants of renewable energy are typically divided into two dimensions, namely renewable energy consumption and renewable energy production. The common determinants of renewable energy supply are oil price, carbon dioxide emissions, coal price, and gas price (Refer to [22,23,24,25,26,27,28,29]), while the demand side is usually determined by energy use, oil price, carbon dioxide emissions, coal price, and gas price (Refer to [30,31,32,33,34,35]). The factors driving the supply and demand of renewable energy are well documented in the recent work of [36] on solar energy in Japan. The present study, however, focused on one specific predictor of renewable energy supply, i.e., IPR. The primary characteristic of IPR is to recognise producers’ efforts in developing or innovating novel creations that contribute to the market. As such, strict property right protection might not be healthy for renewable energy development as it would undermine new market entrants in the renewable energy industry, who would find it difficult to acquire licenses for renewable technology deployment if copyrights have been registered and granted to the founder. If that is the case, IPRs restrict the development of this industry and eventually become a barrier to new investment. According to World Intellectual Property Organization, Intellectual property can incentivise or create obstacles to technology transfer. Though IPR protection can support climate change technology inventors’ innovations, overprotection is a hindrance to high capital funding. Besides IPR, the other explanatory variables included in this study were population, carbon dioxide emission, GDP, energy use, alternative energy electricity generation, and other energy prices. The selection of these variables was motivated by the research of [22,23,24,25] as summarised in

Table 1. Factors driving renewable energy production in the literature.

Explanatory Variable	[22]	[23]	[24]	[25]
GDP	Mixture	-	Neutral	Positive
CO2 Emission	Negative	Negative	Positive	-
Energy Use	Positive	Positive	Negative	Positive
Oil Price	Mixture	-	Mixture	Mixture
Coal Price	Mixture	-	Neutral	Mixture
Gas Price	Positive	-	Neutral	Negative

Note: Positive indicates a positive and significant coefficient; negative indicates a negative and significant coefficient; mixture indicates both positive and negative significant coefficients; neutral indicates non-significant coefficient.

.

Empirical works from [22,23,24,25], which involved regression analyses, did not include the role of IPR in their study. To examine the impact of IPR on renewable energy production, this study modified the work of [22] by positing IPR as the primary explanatory variable. The resultant model is as follows:

$$\begin{array}{cc}\hfill REW{P}_{it}={\alpha }_0+& {\alpha }_{1}PO{P}_{it}+{\alpha }_{2}CO{2}_{it}+{\alpha }_{3}GD{P}_{it}+{\alpha }_{4}EUS{E}_{it}+{\alpha }_{5}OIL\hfill \\ & +{\alpha }_{6}COA{L}_{it}+{\alpha }_{7}GA{S}_{it}+{\alpha }_{8}OIL{P}_{it}+{\alpha }_{9}COAL{P}_{it}+{\alpha }_{10}GAS{P}_{it}+{\alpha }_{11}IP{R}_{it}+{\epsilon }_{it}\hfill \end{array}$$

(1)

where $REW{P}_{it}$ = Renewable energy production

$PO{P}_{it}$ = Population growth

$CO{2}_{it}$ = Carbon dioxide emission

$GD{P}_{it}$ = GDP

$EUS{E}_{it}$ = Energy use

$OI{L}_{it}$ = Oil price

$COA{L}_{it}$ = Coal price

$GA{S}_{it}$ = Gas price

$OIL{P}_{it}$ = Electricity production from oil sources

$COAL{P}_{it}$ = Electricity production from coal sources

$GAS{P}_{it}$ = Electricity production from gas sources

$IP{R}_{it}$ = Intellectual property right

3.2. Methodology

This study employed the dynamic panel, generalised method of moments (GMM) technique, which is an extension of the instrumental variable (IV) approach by [37]. The GMM method is designed to address issues associated with short time series data and potential endogeneity. This technique has become more popular among researchers due to its several advantages. For instance, it can handle panel data with a small number of time span observations “T” with a large number of cross-section units “N”. It also assumes a linear combination functional relationship, where the dynamic left-hand side variable depends on its own past values. As such, the independent variables are not strictly exogenous; rather, they are possibly correlated with the present and past values of the error terms. Moreover, the GMM also assumes fixed individual effects to exist in the model. Finally, it allows for the existence of heteroscedasticity and autocorrelation in the data. Two methods have been developed under the GMM framework, namely difference GMM and system GMM. The difference GMM was initiated by [38] and later developed by [39,40,41]). This study opted to use the system GMM estimator because it has been shown to perform much better than the difference GMM in terms of lower bias and higher precision, especially when autoregressive series are close to the random walk [42]. Prior to the examination of regression model results, Cook’s distance test was performed to identify the outlier. For diagnostic checks, the Sargan test and the Arellano-Bond second-order correlation were employed to validate the results. Although several empirical works have highlighted the impact of institutions or institutional quality, no study has covered the specific scope of this indicator. In particular, the role of IPR, as an element of institutional quality, is the core indicator that requires more evidence to supplement existing studies. The interpretation of the protection level of IPR as a measure of institutional quality was put forth by [43]. IPR measures the degree and importance of legal frameworks in renewable energy development. Stronger IPR protection implies that creation or invention is governed strictly by statutory rights and that pioneering teams’ knowledge is appropriately safeguarded.

3.3. Data

Our panel analysis covered time series data of 59 countries from 1980 to 2014. The complete list of countries is presented in Appendix A as

Table A1. List of countries.

Renewable Energy Electricity Production (WDI)				Renewable Energy Electricity Production (BP)
WIPO	OECD	WGI	ICRG	WIPO	OECD	WGI	ICRG
1. Argentina	1. Argentina	1. Argentina	1. Argentina	1. Argentina	1. Argentina	1. Argentina	1. Argentina
2. Australia	2. Australia	2. Australia	2. Australia	2. Australia	2. Australia	2. Australia	2. Australia
3. Austria	3. Austria	3. Austria	3. Austria	3. Austria	3. Austria	3. Austria	3. Austria
4. Belgium	4. Belgium	4. Bangladesh	4. Bangladesh	4. Bangladesh	4. Belgium	4. Bangladesh	4. Bangladesh
5. Brazil	5. Brazil	5. Belgium	5. Belgium	5. Belgium	5. Brazil	5. Belgium	5. Belgium
6. Bulgaria	6. Bulgaria	6. Brazil	6. Brazil	6. Brazil	6. Bulgaria	6. Brazil	6. Brazil
7. Canada	7. Canada	7. Bulgaria	7. Bulgaria	7. Bulgaria	7. Canada	7. Bulgaria	7. Bulgaria
8. Chile	8. Chile	8. Canada	8. Canada	8. Canada	8. Chile	8. Canada	8. Canada
9. China	9. China	9. Chile	9. Chile	9. Chile	9. China	9. Chile	9. Chile
10. Colombia	10. Colombia	10. China	10. China	10. China	10. Colombia	10. China	10. China
11. Croatia	11. Croatia	11. Colombia	11. Colombia	11. Colombia	11. Croatia	11. Colombia	11. Colombia
12. Czech Republic	12. Czech Republic	12. Croatia	12. Croatia	12. Croatia	12. Czech Republic	12. Croatia	12. Croatia
13. Denmark	13. Denmark	13. Czech Republic	13. Czech Republic	13. Czech Republic	13. Denmark	13. Czech Republic	13. Czech Republic
14. Estonia	14. Estonia	14. Denmark	14. Denmark	14. Denmark	14. Estonia	14. Denmark	14. Denmark
15. Finland	15. Finland	15. Dominican Republic	15. Dominican Republic	15. Estonia	15. Finland	15. Estonia	15. Estonia
16. France	16. France	16. Estonia	16. Estonia	16. Finland	16. France	16. Finland	16. Finland
17. Germany	17. Germany	17. Finland	17. Finland	17. France	17. Germany	17. France	17. France
18. Greece	18. Greece	18. France	18. France	18. Germany	18. Greece	18. Germany	18. Germany
19. Hong Kong	19. Hong Kong	19. Germany	19. Germany	19. Greece	19. Hong Kong	19. Greece	19. Greece
20. Hungary	20. Hungary	20. Greece	20. Greece	20. Hong Kong	20. Hungary	20. Hong Kong	20. Hungary
21. India	21. India	21. Hong Kong	21. Hungary	21. Hungary	21. India	21. Hungary	21. India
22. Indonesia	22. Indonesia	22. Hungary	22. India	22. India	22. Indonesia	22. India	22. Indonesia
23. Iran.	23. Iran	23. India	23. Indonesia	23. Indonesia	23. Iran	23. Indonesia	23. Ireland
24. Ireland	24. Ireland	24. Indonesia	24. Ireland	24. Iran	24. Ireland	24. Iran	24. Israel
25. Israel	25. Israel	25. Iran	25. Israel	25. Ireland	25. Israel	25. Ireland	25. Italy
26. Italy	26. Italy	26. Ireland	26. Italy	26. Israel	26. Italy	26. Israel	26. Japan
27. Japan	27. Japan	27. Israel	27. Japan	27. Italy	27. Japan	27. Italy	27. Kazakhstan
28. Korea, Rep.	28. Korea, Rep.	28. Italy	28. Kazakhstan	28. Japan	28. Korea, Rep.	28. Japan	28. Korea, Rep.
29. Latvia	29. Latvia	29. Japan	29. Korea, Rep.	29. Kazakhstan	29. Latvia	29. Kazakhstan	29. Latvia
30. Luxembourg	30. Malaysia	30. Kazakhstan	30. Latvia	30. Korea, Rep.	30. Malaysia	30. Korea, Rep.	30. Luxembourg
31. Malaysia	31. Mexico	31. Korea, Rep.	31. Luxembourg	31. Latvia	31. Mexico	31. Latvia	31. Malaysia
32. Mexico	32. Morocco	32. Latvia	32. Malaysia	32. Luxembourg	32. Morocco	32. Luxembourg	32. Mexico
33. Morocco	33. Netherlands	33. Luxembourg	33. Mexico	33. Malaysia	33. Netherlands	33. Malaysia	33. Morocco
34. Netherlands	34. New Zealand	34. Malaysia	34. Morocco	34. Mexico	34. New Zealand	34. Mexico	34. Netherlands
35. New Zealand	35. Norway	35. Mexico	35. Netherlands	35. Morocco	35. Norway	35. Morocco	35. New Zealand
36. Norway	36. Pakistan	36. Morocco	36. New Zealand	36. Netherlands	36. Pakistan	36. Netherlands	36. Norway
37. Philippines	37. Peru	37. Netherlands	37. Norway	37. New Zealand	37. Peru	37. New Zealand	37. Pakistan
38. Poland	38. Philippines	38. New Zealand	38. Pakistan	38. Norway	38. Philippines	38. Norway	38. Peru
39. Portugal	39. Poland	39. Norway	39. Peru	39. Philippines	39. Poland	39. Pakistan	39. Philippines
40. Romania	40. Portugal	40. Pakistan	40. Philippines	40. Poland	40. Portugal	40. Peru	40. Poland
41. Russian Federation	41. Romania	41. Peru	41. Poland	41. Portugal	41. Romania	41. Philippines	41. Portugal
42. Serbia	42. Russian Federation	42. Philippines	42. Portugal	42. Romania	42. Russian Federation	42. Poland	42. Romania
43. Singapore	43. Singapore	43. Poland	43. Romania	43. Russian Federation	43. Singapore	43. Portugal	43. Singapore
44. Slovak Republic	44. Slovak Republic	44. Portugal	44. Serbia	44. Singapore	44. Slovak Republic	44. Romania	44. Slovenia
45. Slovenia	45. Slovenia	45. Romania	45. Singapore	45. Slovak Republic	45. Slovenia	45. Russian Federation	45. Spain
46. Spain	46. Spain	46. Russian Federation	46. Slovenia	46. Slovenia	46. Spain	46. Singapore	46. Sweden
47. Sweden	47. Sweden	47. Serbia	47. Spain	47. Spain	47. Sweden	47. Slovak Republic	47. Switzerland
48. Switzerland	48. Thailand	48. Singapore	48. Sweden	48. Sweden	48. Thailand	48. Slovenia	48. Thailand
49. Thailand	49. Turkey	49. Slovak Republic	49. Switzerland	49. Switzerland	49. Turkey	49. Spain	49. Turkey
50. Turkey	50. Ukraine	50. Slovenia	50. Tanzania	50. Thailand	50. Ukraine	50. Sweden	50. Ukraine
51. Ukraine	51. United Kingdom	51. Spain	51. Thailand	51. Turkey	51. United Kingdom	51. Thailand	51. United Kingdom
52. United Kingdom	52. United States	52. Sweden	52. Turkey	52. Ukraine	52. United States	52. Turkey	52. United States
53. United States		53. Tanzania	53. Ukraine	53. United Kingdom		53. Ukraine	53. Vietnam
54. Vietnam		54. Thailand	54. United Kingdom	54. United States		54. United Kingdom
		55. Turkey	55. United States	55. Vietnam		55. United States
		56. Ukraine	56. Vietnam			56. Vietnam
		57. United Kingdom
		58. United States
		59. Vietnam

. Two proxies were used to represent the dependent variable of this study: (i) electricity production from renewable sources (REWDI) retrieved from WDI; and (ii) renewable energy generation (REBP) obtained directly from the BP Statistical Review of World Energy. The former was measured as the percentage of total electricity production while the latter was measured as Terawatt-hours. Data of patents granted by both the OECD and WIPO were used to assess IPR. As IPR is one of various governance factors, institutional quality represented by the ICRG and WGI were also used to evaluate institutional quality’s impact on renewable energy production. Institutional quality is notably known as one of the key drivers of renewable energy industry growth [44]. The data for the remaining control variables were primarily collected from WDI and World Bank. After collecting the data for all the variables involved, conversion to logarithmic form was performed to reduce the variation among data. For certain data that are in negative value, an upward adjustment was created such that it allows for the conversion to logarithmic form.

Table A2. Definitions, data sources, and descriptions.

Variable		Definition	Source	Unit Measurement	Mean	Std Dev	Min	Max
RE	REWP	Electricity Production from Renewable sources	WDI	% of Total Electricity Production	3.75	5.79	0.00	55.85
	REWPBP	Renewables Energy Generation	BP	Terawatt-hours	8.78	23.84	0.00	296.78
	POP	Population Growth	WDI	% change	0.74	0.76	−2.08	2.89
	CO2	CO2 emissions	WDI	Metric tons per capita	7.23	4.67	0.14	27.43
	GDP	Real Gross Domestic Product	WDI	US$ constant price 2010	1050	2090	10	16,200
	EUSE	Energy Use	WDI	KG of oil equivalent per capita	3095.35	1971.51	163.94	9353.42
OP	OIL	Oil Price	WB	US$ Price	49.83	33.41	13.53	103.40
	COAL	Coal Price	WB	US$ Price	54.48	30.06	25.08	123.85
	GAS	Gas Price	WB	US$ Price	5.02	2.58	1.89	11.13
OE	OILP	Electricity Production from Oil sources	WDI	% of Total Electricity Production	8.29	12.01	0.00	71.44
	COALP	Electricity Production from Coal sources	WDI	% of Total Electricity Production	29.54	25.07	0.00	97.49
	GASP	Electricity Production from Gas sources	WDI	% of Total Electricity Production	20.54	19.50	0.03	95.27
IPR	IPR WIPO	Patent Granted to Environmental Technologies	WIPO	Actual Count	241.29	598.68	1.00	4474.00
	IPR OECD	Patent Granted to Climate Change Mitigation Technologies	OECD	Actual Count	96.27	314.52	0.08	2774.35

Notes: WDI, World Development Indicators; BP, BP Statistical Review of World Energy; WB, World Bank Commodity Price Data; WIPO, World Intellectual Property Organization; OECD, Organisation for Economic Co-operation and Development.

in the Appendix A presents definitions, sources, and descriptive statistics. Appendix A,

Table A3. Correlation Matrix.

Variables	REWP	REWPBP	POP	CO2	GDP	EUSE	OIL	COAL	GAS	OILP	COALP	GASP	IPR WIPO	IPR OECD
REWP	1.00
REWPBP	0.14	1.00
POP	−0.14	−0.02	1.00
CO2	−0.09	0.24	0.06	1.00
GDP	−0.11	0.81	0.02	0.38	1.00
EUSE	−0.01	0.18	0.03	0.85	0.28	1.00
OIL	0.33	0.21	0.00	−0.12	0.00	−0.09	1.00
COAL	0.23	0.13	−0.01	−0.09	−0.01	−0.07	0.88	1.00
GAS	0.15	0.08	0.01	−0.05	−0.01	−0.05	0.75	0.73	1.00
OILP	−0.14	−0.12	0.20	−0.19	−0.04	−0.32	−0.17	−0.14	−0.11	1.00
COALP	−0.15	0.15	−0.02	0.17	0.17	−0.19	−0.07	−0.06	−0.03	−0.01	1.00
GASP	0.04	−0.07	0.19	−0.06	−0.05	−0.16	0.12	0.11	0.07	0.26	−0.25	1.00
IPR WIPO	−0.13	0.54	−0.16	0.24	0.65	0.16	0.07	0.03	0.02	0.04	0.15	−0.05	1.00
IPR OECD	−0.06	0.68	−0.04	0.42	0.85	0.34	0.06	0.09	0.04	−0.03	0.11	0.01	0.61	1.00

Note: REWP, Electricity Production from Renewable Sources; REWPBP, Renewable Energy Generation; POP, population growth; CO₂, CO₂ emission; GDP, real gross domestic products; EUSE, energy ese; OIL, oil price; Coal, coal price; Gas, gas price; OILP, electricity production from oil; COALP, electricity production from coal; GASP, electricity production from gas; IPR WIPO, patent granted to environmental technologies; IPR OECD, patent granted to climate mitigation technologies.

also presents the correlation results for the model with renewable energy production as the dependent variable.

4. Results and Discussion

4.1. Main Result

The results of the impact of IPR on renewable energy production are shown in

Table 2. Results of dynamic panel estimations for intellectual property rights and renewable energy production.

VARIABLES	Renewable Energy Production (WDI)		Renewable Energy Production (BP)
	Model 1	Model 2	Model 3	Model 4
L.REWP	0.799 ***	0.687 ***	0.676 ***	0.309 ***
	(0.0194)	(0.0135)	(0.00603)	(0.0177)
POP	0.0617 ***	0.101 ***	0.0404 ***	0.207 ***
	(0.0152)	(0.0150)	(0.0117)	(0.0190)
CO2	−0.538	−1.300 ***	−0.714	−0.894 ***
	(0.444)	(0.448)	(0.577)	(0.304)
GDP	−0.0678	−0.0424	0.118	0.310 ***
	(0.0437)	(0.0744)	(0.0836)	(0.0578)
EUSE	0.775 *	1.520 ***	1.162 *	1.124 ***
	(0.411)	(0.473)	(0.689)	(0.235)
OIL	0.475 ***	0.541 ***	0.426 ***	1.426 ***
	(0.0437)	(0.0939)	(0.0690)	(0.0606)
COAL	−0.0653 ***	−0.122 ***	−0.0554 ***	−0.501 ***
	(0.0247)	(0.0342)	(0.0209)	(0.0344)
GAS	−0.286 ***	−0.410 ***	−0.295 ***	−0.860 ***
	(0.0360)	(0.0550)	(0.0785)	(0.0468)
OILP	−0.0516 ***	−0.00326	−0.110 ***	−0.0651 ***
	(0.0179)	(0.00976)	(0.0185)	(0.0175)
COALP	−0.0116	0.217 ***	−0.0539	−0.0612
	(0.0271)	(0.0417)	(0.0380)	(0.0393)
GASP	0.0609 ***	0.0505 ***	0.0610 ***	−0.0148
	(0.00927)	(0.00794)	(0.0116)	(0.0198)
IPR WIPO	0.0393 ***		0.133 ***
	(0.00811)		(0.00870)
IPR OECD		0.0128		0.113 ***
		(0.00843)		(0.0146)
Constant	−5.451 **	−10.51 ***	−10.18 **	−11.98 ***
	(2.595)	(3.654)	(5.077)	(1.273)
Observations	823	466	885	468
N	51	48	52	47
Sargan Test	42.1210	31.9784	39.3982	31.9288
	1.0000	1.0000	1.0000	1.0000
AR(1)	−2.7873	−1.6598	−1.5002	−1.9000
	0.0053	0.0970	0.1336	0.0574
AR(2)	−1.1821	0.2257	−1.4329	−0.5775
	0.2372	0.8214	0.1519	0.5636

Note: *, **, *** denote the rejection of the null hypothesis at 1%, 5%, and 10% significance levels, respectively. Standard errors are in parentheses. Constant term included in all specifications. Time dummies were jointly significant and were not reported here for brevity. AR(2) is a test of second-order residual serial correlations. The Sargan test is for overidentification.

. Model 1 is our main model, which used patents granted by WIPO to environmental technologies as the proxy for IPR. Model 2 employed another IPR indicator, which is patent granted by OECD to climate change mitigation technologies. Models 3 and 4 utilised renewable energy production data from BP as a robustness check for the primary model. All the estimated models passed the Sargan test and confirmed that there was no serial correlation issue in the AR2 test. The common procedure of identifying outliers and excluding them from the regression was applied to avoid bias in the results.

Model 1 in Table 2 used patents granted as the proxy for IPR. As shown in Table 2, population growth is an important factor that triggers renewable energy production, evidenced by its positive coefficient. This finding acknowledges the considerable role of population growth in energy demand. However, an unanticipated finding was the negative coefficient of CO₂, suggesting that severe pollution levels do not exert a strong impetus on investments in renewable energy. As suggested by [23], this negative effect may mean greater dependency on traditional resources to generate energy. With respect to the role of GDP in renewable energy production, the findings reported a non-significant negative coefficient, which revealed a similar finding with [45,46,47]. Ref. [45] argued that effects of renewable energy on growth can be negative or positive but statistically can be insignificant. A country’s characteristics can be known as the primary factor that contributes to the discouragement of renewable energy development, such as India, which continues to heavily rely on coal as their source of energy. Top oil reserves prove that certain countries in the world, such as Brazil, Canada, and Iran, may find the balance between the investment renewable energy and non-renewable energy difficult, given that there is an abundance of oil. Last but not least, countries without appropriate support from their national energy council can further restrict the development of renewable energy, such as in Indonesia, whereby investors face financing barriers as well as policy uncertainty issues. The findings in this study imply that renewable energy may not be the primary investment option for investors with personal income growth for several reasons. First, investors may find that renewable energy investments do not offer lucrative returns such as well-established non-renewable energy investments because of their higher upfront capital. Second, it takes a longer time for renewable energy investments to meet the break-even point. For an investor aiming for short term returns, this lengthy time horizon is not a favourable option. Third, to participate in renewable energy project bidding, proposals need to be specific and have reasonable cost estimates, as most such projects are awarded by the government. As such, the investment route is longer and more tedious, with uncertain outcomes; this may discourage investment in the renewable energy sector.

Energy use was statistically significant with a positive coefficient, reiterating that the increase in energy use needs be supported by renewable energy generation. A similar finding was obtained by [29], who confirmed that energy use or consumption has a positive impact on renewable energy deployment. Our evidence further showed mixed findings for fossil fuel prices, whereas oil price had a positive coefficient, gas and coal prices had negative coefficients. The positive coefficient of oil price confirms that the appreciation of oil price boosts renewable energy generation. Addressing the electricity production of fossil fuel resources, the results obtained were interesting. Oil and coal electricity production exhibited a negative relationship with renewable energy production. When electricity production from oil and coal is higher, more factors of production are required to support their operations. As a result, electricity production from renewable energy experiences resource scarcity as factors of production are allocated to electricity production by traditional resources. The result of gas energy electricity production was positive, contrary to the former two variables, indicating that the increase in gas production stimulates the electricity production of renewable energy.

The variable that represented IPR was patents granted to renewable energy inventions, which reported a positive and statistically significant result. The conclusion can thus be drawn that IPR is an important determinant of renewable energy production. When more protection is given to renewable energy firms, investors feel more secure and confident to expand their business as their efforts (e.g., wind farm design, solar panel installation technique, biomass processing method, etc.) are recognised. Typically, these efforts require extensive R&D and time; therefore, investors clearly want to protect their property rights to prohibit unauthorised imitation of their methodology in generating energy. With the establishment of strong protection to assure property rights, there are higher injections of capital to promote renewable energy production. There are several factors behind the finding against the result produced by [9], such as the dependent variable employed in this preceding work, renewable energy consumption. The proxy employed to represent IPR only emphasises the Ginarte-Park Index, while this study examined the factor of IPR via two different proxies, and lastly, the number of countries involved in the study are also different.

To evaluate the reliability of the results produced in Model 1, this study employed another IPR indicator, patents granted by OECD to climate change mitigation technologies, shown in Model 2 in Table 2. This model, however, did not report a significant finding for IPR, although the coefficient was positive. One of the plausible factors that contributed to such a result could be due to the number of time series data included, which include a shorter time span compared to the first model. Since Model 2 failed to prove the effect of IPR on renewable energy production, Model 3 and Model 4 were developed using renewable energy production data from BP as the dependent variable. Based on the results, there is solid evidence to show that IPR plays a critical role in driving renewable energy production. Our finding implies that when the degree of protection is higher in the renewable energy sector, such as by granting assurance to renewable technology inventors, inventors gain confidence in contributing ideas that can enhance electricity generation efficiency from renewable resources. With such interventions, more renewable energy technologies will be introduced, which will subsequently secure the capital required by the renewable energy industry.

4.2. Robustness Check

Since IPR is an institutional factor, this study re-estimated the models in Table 2 by replacing IPR with institutional factor data from WGI and ICRG as a robustness check. The results of the robustness check are reported in

Table 3. Results of institutional quality and renewable energy production.

VARIABLES	Renewable Energy Production (WDI)		Renewable Energy Production (BP)
	Main Model Model 1	Model 2	Model 3	Model 4
L.REWP	0.847 ***	0.909 ***	0.396 ***	0.706 ***
	(0.0101)	(0.0112)	(0.0158)	(0.0118)
POP	0.0992 ***	0.0580 ***	0.302 ***	0.0329 ***
	(0.0202)	(0.0178)	(0.0256)	(0.0104)
CO2	−0.511 ***	−0.688	−0.144	−0.751 **
	(0.184)	(0.618)	(0.188)	(0.333)
GDP	−0.259 ***	−0.0891	0.779 ***	0.362 ***
	(0.0366)	(0.225)	(0.0783)	(0.0966)
EUSE	0.322	0.955	−0.743 ***	1.016 ***
	(0.250)	(0.624)	(0.264)	(0.385)
OIL	0.363 ***	0.168 ***	0.766 ***	0.302 ***
	(0.0555)	(0.0489)	(0.0794)	(0.0303)
COAL	−0.0743 ***	0.0521 ***	−0.149 ***	0.000557
	(0.0231)	(0.0197)	(0.0263)	(0.0133)
GAS	−0.329 ***	−0.179 ***	−0.795 ***	−0.192 ***
	(0.0406)	(0.0415)	(0.0618)	(0.0197)
OILP	−0.0158	0.00261	−0.272 ***	−0.131 ***
	(0.0102)	(0.0274)	(0.0166)	(0.0206)
COALP	0.162 ***	0.108 **	0.0503	0.0760 ***
	(0.0356)	(0.0463)	(0.0333)	(0.0244)
GASP	0.0199	0.0560 **	−0.176 ***	0.0530 ***
	(0.0164)	(0.0227)	(0.0377)	(0.0131)
IQ WGI	0.835 ***		0.859 ***
	(0.177)		(0.316)
IQ ICRG		−0.202		0.0200
		(0.332)		(0.151)
Constant	1.142	−4.315	−18.19 ***	−17.01 ***
	(1.953)	(6.256)	(2.064)	(2.402)
Observations	484	932	570	1025
N	52	50	55	52
Sargan Test	41.13089	34.4894	43.17009	41.79858
	(1.0000)	(1.0000)	(1.0000)	(1.0000)
AR(1)	−2.4114	−2.8674	−2.3083	−1.4363
	(0.0159)	(0.0041)	(0.0210)	(0.1509)
AR(2)	−0.24241	−0.91367	−1.1249	−1.0434
	(0.8085)	(0.3609)	(0.2606)	(0.2968)

Note: *, **, *** denote the rejection of the null hypothesis at 1%, 5%, and 10% significance levels, respectively. Standard errors are in parentheses. Constant term included in all specifications. Time dummies were jointly significant and were not reported here for brevity. AR(2) is a test of second-order residual serial correlations. The Sargan test is for overidentification.

, which shows that all four models passed the diagnostic checks. The results appeared to be robust with the alternative proxy, as indicated by most of the coefficients estimated, carrying similar signs. In particular, our main interest variable (IQ WGI) exhibited statistically significant results. However, the coefficient of IQ ICRG was statistically insignificant. With respect to the positive finding produced by our main model (Model 1) and Model 3, an important implication is that a better institutional framework will assure environmental legislation is strictly regulated; hence, it will strengthen investors’ confidence and facilitate renewable energy investment. Investors weigh local statutory standards rigorously, thus the existence of regulatory protection and standard assurance will drive environmentally friendly investments. This finding raises the notion that the commitment of central governments to combat climate change is vital in promoting the deployment of renewable energy.

Table 4. Summary of results of intellectual property right and institutional quality.

Variables	REWP WDI		REWP BP		REWP WDI		REWP BP
	Model 1	Model 2	Model 3	Model 4	Model 1	Model 2	Model 3	Model 4
POP	+(*)	+(***)	+(NS)	+(***)	+(***)	+(***)	+(***)	+(***)
CO2	+(NS)	−(***)	−(NS)	−(***)	−(NS)	−(***)	−(NS)	−(***)
GDP	+(NS)	−(NS)	+(**)	+(***)	+(***)	+(***)	+(***)	+(***)
ENERGYUSE	−(NS)	+(***)	+(NS)	+(***)	−(***)	+(***)	−(***)	+(***)
OIL	+(***)	+(***)	+(***)	+(***)	+(***)	+(***)	+(***)	+(***)
COAL	−(**)	−(***)	−(***)	−(***)	−(***)	−(NS)	−(***)	−(NS)
GAS	−(***)	−(***)	−(***)	−(***)	−(***)	−(***)	−(***)	−(***)
OILP	−(***)	+(NS)	−(***)	−(***)	−(***)	−(***)	−(***)	−(***)
COALP	+(NS)	+(***)	−(NS)	+(NS)	+(NS)	+(***)	+(NS)	+(***)
GASP	+(***)	+(***)	+(***)	−(NS)	−(***)	+(***)	−(***)	+(***)
IPR WIPO	+(***)		+(***)
IPR OECD		+(NS)		+(***)
IQ WGI					+(***)		+(***)
IQ ICRG						−(NS)		+(NS)

Note: The table reports a summary of the relationship between all the financial development indicators associated with renewable energy and growth using the two-Step GMM methodology. NS refers to ‘not statistically significant’. * Represents significant at 10%, ** represents significant at 5%, and *** represents significant at 1%.

provides a complete summary of the analysis results, which provide strong evidence that IPR drives renewable energy electricity production. This finding was supported by the results of the robustness check model that considered institutional quality as the proxy for IPR.

5. Conclusions and Policy Implications

The debate on the role of IPR protection scheme in the renewable energy sector from multiple aspects remains an on-going issue. Is introducing safeguards an effective approach to promote the development of this sector? The answer to this question will be left to research related to this field of study. Generally, excessive IPR protection mechanisms will not be a good option while the least protected may turn down the interest of renewable energy investors. Given this, the study conducted is expected to produce important feedback to the renewable industry in respect to whether the protection or its development as well as the degree of IPR protection are a must. It has been demonstrated that renewable energy has the potential to be a useful input in the pursuit of sustainable economic growth [48]. However, deciding the shift from fossil-fuel-based energy to renewable energy production may be difficult because most renewable energy projects require high projected up-front costs, competitive technologies, and longer payback times for return on investment. Thus, the role of the government in intellectual property right (IPR) enforcement is highly important in protecting the developers’ and investors’ rights and facilitating the development of renewable energy. This paper examined the effect of IPR on renewable energy electricity production using the dynamic panel system GMM analysis on data from 59 countries over the period from 1986 to 2014. The role of IPR was proxied by patents granted to environmental technologies and renewable technologies. This study proved that IPR is an important pillar in sustaining renewable energy development. The results were also robust when the alternative proxy of IPR and institutional quality was tested. The existence of IPR protection reflects that innovation via R&D is regarded as crucial to stimulate investment in the renewable energy sector. Without policies to govern IPR, technology developers may be reluctant to share information, causing further delays in investment and technology transfer. When no party is willing to move first and would rather wait for benefits or imitations, the problem of delays in investment will worsen due to the existence of such ‘free riders’ [49]. As IPR prevents the ‘free rider problem’ in this industry, renewable energy firms will feel more secure to launch their development plans. Protection granted to such big scale projects enforces rules and regulations that must be adhered to by potential investors. Any violation of these rules and regulations in the new investment scheme will cause the project to be withdrawn from the plan. Consequently, when IPR protection is rigorous, firms or developers are more willing to disseminate knowledge on renewable energy, thereby encouraging technology transfer in the industry.

Moreover, capital funding to support research on the efficiency of electricity generation in the renewable energy industry is heavily dependent on the value of ideas, and more importantly, a country’s intention to combat climate change. Hence, it is a positive sign when there is a clean energy transformation plan for a nation, because clean renewables are indispensable to secure sustainable energy supply in a majority of countries. However, as suggested by [50], given that most renewables are intermittent sources of energy, it is advisable to consume them via continuous forms of energy (e.g., hydro and geothermal) to ensure the availability of other non-renewable energy such as oil, coal, and gas for commercial, industrial, residential, and agricultural activities.

The policy implication is straightforward in this study. IPR enforcement is a form of protection that reinforces confidence in renewable energy investment, which is deemed important for this industry involved in multiple technologies. For instance, to produce a greater amount of electricity from solar energy farms, the quality of solar panels is critically important. If a solar panel is highly efficient in absorbing energy from the sun, the solar panel design may be sold to other firms. In order to do so, the firm that created the solar panel will file for patent protection to safeguard their invention. In essence, IPR demonstrates that the original efforts of the inventors are recognised. For policy makers, energy transformation measures should be undertaken, such as the introduction of monetary incentives or corporate tax reduction measures to develop the renewables industry. Future studies can extend to specific energy generation types such as solar, wind, biomass, and others. Examining specific energy types may enable forthcoming empirical works to identify the core energy sector that is most affected by IPRs. Moreover, future research can also specialise in the area of stock market development and renewable energy production due to the stock market proving to lead to more renewable energy use [51]. Hence, stock market development may be a potential driving force for renewable energy production.

Overall, this paper has covered the issue regarding the influence of intellectual property rights to renewable energy. Nevertheless, this study has limitations worth mentioning, the most important is that it does not consider subsidies and other incentives provided by governments. Many countries show a surge in interest in renewable energy sources after the introduction of subsidies and other incentives by governments. Due to the data of subsidies and other incentives by governments being qualitative in nature, this study did not consider them in our model.