Industrial adoption and transition towards sustainable renewable energy

Abstract

A global revolution is called upon to transition into renewable sources of energy from fossil fuels in an accelerated manner by various countries to become net zero carbon emitter with self-committed timelines. This study identified carbon dioxide emissions and participation of renewable energy in total energy supply as two major dimensions and discusses the impact of various independent variables quantitively by multipanel regression in timeseries and countries for comparative correlation analysis. Theme based industry expert interviews adds qualitative dimension to the study. The results suggest that country’s energy need and availability of fossil fuels within, determines the RE adoption rate, while countries with high reliance on coal and crude are shifting towards RE sources. Investments in research and development in hydrogen, fuel cells and storage are yet to make an impact in this direction since they are recent in this long game of energy transition.

Keywords

Sustainable renewable energy quantitative qualitative data modelling statistics Eviews word cloud

1. Introduction

An accelerated mission or as some would call revolution is called upon by all major participating economies (at least) of the world to shift their dependence for energy from existing towards renewable sources of energy like solar, wind, biomass etc. to achieve net zero carbon emission target within time self-committed by them.

This study delves into this subject to understand the importance, urgency, current work, and literature studies conducted, major steps already taken or in progress. The importance of carbon dioxide emissions and participation of renewable energy in total energy supply stands out as two major dimensions. This work explored, identified, evaluated, and established various independent variables which might be impactful to these two dimensions. These factors span demographic, economic, energy source import, export, supply and consumption, investments committed to technology development for various sources, GHG and CO2 emissions etc. Both qualitative and quantitative methods were adopted to conduct this study.

Theme based interviews of subject area experts were conducted to capture their opinions and analysis of the subject. The transcribed interview recordings on performing word frequency analysis showed that industry experts see energy transition happening in a big way but not very soon, a lot of emphasis was given on need for regulations, policies, price equilibrium between fossil and renewable energy sources, they were skeptical on the pace with which the price of renewable energy drops, and infrastructure will replace conventional energy production.

For quantitative evaluation, data from 1990–2019 for 15 largest economies of the world (based on GDP) were compared and multi-panel timeseries regression analysis was conducted in EViews 12 student version.

2. Literature review

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Mutezo et al. (2021) found that driven by industrialization and population growth; Africa’s energy demand would increase tremendously. Renewable energy sources are major energy sources for most of sub-Saharan Africa. But Algeria, Nigeria, Morocco, South Africa and Egypt (the Big Five) are significantly dependent on fossil fuels. Increased renewable energy uptake and transition can be achieved by circular economy approach.
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Zhao et al. (2018) discusses that as the largest energy consumption country China, is facing a multitude of energy related challenges including energy sustainability, global warming and environmental pollution, which has prompted China to promote renewable energy (RE) utilization proactively. This study conducted and demonstrate a systematical investigation on critical factors (CFs) affecting REPG development. They identified 43 influence factors for REPG development through multi-facet content analysis. Followed by 33 CFs affecting REPG development which were prioritized by the help of conducting questionnaire survey and relative importance index (RII) model. By utilizing principal component analysis (PCA) they condensed the CFs to 12 principal components (PCs).
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Aguirre et al. (2014) with the help of FEVD and PCSE estimation methods this study investigated factors influencing country- level renewable energy growth. This study claims to have taken a longer timeseries (1990–2010) and a broader sample size of than previous studies, the results have given new directions. They observed that approaches that were introduced to satisfy public demand for more sustainable investments and programmes are weak and negatively influence the growth of renewables.
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Marques et al. (2010) has identified that there is limited empirical analysis about drivers promoting renewables despite the increasing amount of literature and focus on renewable energy. The results indicate that both the lobby of the traditional energy sources (oil, coal, and natural gas) and CO2 emissions did restrain renewable deployment. The purpose of reducing energy dependency appears to stimulate renewable energy utility.

3. The research problem

Based on the literature review and the directional themes recognized in them, we have concluded two major parameters (dependent variable, DV) which indicates SRE adoption in industry i.e

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Dependent variable 1 (DV1) – Carbon emissions (metric tons per capita)
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Dependent variable 2 (DV2) – Renewable energy as % of total national energy supply

Hence, this study has developed hypothesis (number as mentioned in result table column) for dependence of various critical factors (independent variables, IDV) which might be of impact on these two parameters. We evaluated which of these critical factors (IDVs) have high impact on the respective parameters (DV) and establish their relationship with literature available on the subject.

The IDVs were run for correlation evaluation via multi-panel timeseries regression analysis in Eviews 12 (Student version) software.
4. Research design and methodology

4.1 Qualitative evaluation

Identified major themes related to sustainable renewable energy adoption. On these themes conducted 15 min audio (digital) focused interviews with subject matter experts (SMEs), senior experts working in energy and allied fields with relevant knowledge and opinion on the subject.

The procedure for interview conducts and transcription analysis followed structured manner where experts with experience in energy sector in close network of author identified, contacted to fix online audio interviews. These audio interview files were transcribed in online software website temi.com. The transcribed files were then organized on basis of theme and questions enquired. These text files were imported in Nvivo software trial version and through its text analysis feature-word frequency query was run where all the irrelevant words were added to ’stop word list. Finally the output results-word cloud based on word weightage was achieved.

The interviews transcripts (processed through software available online) was processed and evaluated through qualitative tools like NVIVO etc. This helped reinforce and rationalize the correlations identified via quantitative data evaluation, develop trends in implementation in industry, etc.

4.2 Quantitative evaluation

We have conducted search and collected secondary data from various sources for major economies of the world (top 15 on basis of GDP) for variables spanning energy, economic, social and investment avenues of the countries for at least last 20 years.

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Academic literature. A systematic search was carried out in major databases like ScienceDirect etc. by searching for keywords such as “renewable energy”, “new energy”, “power generation” and “critical affecting/influencing factors” to locate relevant academic papers. On that basis, a comprehensive list of high influence factors for the research question from high impact energy journals, was formulated.
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Databanks and Policy documents. This work studied the datasets, essay and articles on policies, plans and opinions about the subject in various literature and reports published by agencies like International Energy Administration (IEA), NITI Aayog. Other important sources are the World Data Bank, Euro- stat, BP Statistical Review Worlds Energy, UN Data and Solar and Wind Energy Resource Assessment. Through this, data for top 15 economies based on GDP from 1990–2019 was organized for the high influence factors identified through literature review.

This data was then cleaned, processed and evaluated through software i.e EViews for the multipanel regression in timeseries and countries for comparative correlation analysis which led to identification of main significant factors from whole set of critical factors which impact the research question. This data through MS Excel was utilized for identifying underlying correlations which might not be apparent and visible based on the collected data.
5. Findings and discussion

5.1 Results and discussion on expert interviews

Online telephonic audio interviews of 10 industry expert’s majority of them being senior scientists and consultants from oil and gas sector; were transcribed and analyzed in word text analyzer software-Nvivo-QSR.

These word clouds demonstrate the dominant words that came up with increased frequency (and hence weight in the diagram) in the discussions. Irrelevant words were sorted out of the mix. (Refer-Fig. 1-Word cloud result from qualitative analysis by Nvivo-QSR software).

Figure 1.

Word cloud result from qualitative analysis by Nvivo-QSR software.

5.2 Discussion on results from Eviews

Below tables were developed to better understand the results from EViews.

Significant and positive IDVs wrt DV are colored in green; and significant and negative IDVs wrt DV are colored in red.

CO2 emissions are impacted –

(Refer-Table 1-For DV1 i.e CO2 emissions (metric tons per capita) results from EViews).

Table 1
For DV1 i.e CO2 emissions (metric tons per capita) results from EViews

DV1-CO2 emissions (metric tons per capita)
Hypothesis/IDVs	IDV	Coefficient	$t$ -stat	$p$ -value	Relation with DV	Coefficient
Control factors	GDP (current US$)
	Population density (people per sq. km of land area)
	Urban population growth (annual %)
	Tax revenue (% of GDP)
H1	Electric power consumption (kWh per capita)	0.000	1.111	0.267	NS	Positive
H2	Forest area (sq. km)	0.000	$-$ 0.503	0.615	NS	Negative
H3	Industry (including construction), value added (% of GDP)	0.016	1.778	0.076	NS	Positive
H4	High-technology exports (% of manufactured exports)	$-$ 0.022	$-$ 2.463	0.014	*	Negative
H5	Agriculture, forestry, and fishing, value added (% of GDP)	0.090	6.663	0.000	***	Positive
H6	Energy use (kg of oil equivalent per capita)	0.003	28.182	0.000	***	Positive
H7	Coal, peat and oil shale, Total energy supply (PJ)	0.000	4.433	0.000	***	Positive
H8	Crude, NGL and feedstocks, Total energy supply (PJ)	0.000	0.163	0.871	NS	Positive
H9	Oil products, Total energy supply (PJ)	0.000	2.678	0.008	NS	Positive
H10	Natural gas, Total energy supply (PJ)	0.000	4.404	0.000	***	Positive
H11	Nuclear, Total energy supply (PJ)	$-$ 0.001	$-$ 20.673	0.000	***	Negative
H12	Electricity, Total energy supply (PJ)	0.000	0.027	0.978	NS	Positive
H13	GHG emissions from fuel combustion – Coal	$-$ 0.005	$-$ 4.827	0.000	***	Negative
H14	GHG emissions from fuel combustion – Oil	0.003	3.135	0.002	**	Positive
H15	GHG emissions from fuel combustion – Gas	$-$ 0.008	$-$ 4.733	0.000	***	Negative
Color code implies				Significant		Negative
Color code implies				Significant		Positive
$p$ -value $<$ 0.001	Very high significance	***
$p$ -value $<$ 0.01	High significance	**
$p$ -value $<$ 0.05	Low significance	*
$p$ -value $>$ 0.05	Not significant	NS

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Positively by increase in agriculture, forestry, fishing contribution to GDP. Agriculture, Forestry, and Other Land Use accounted for 24% of 2010 global greenhouse gas emissions. Greenhouse gas emissions from this sector come mostly from agriculture (cultivation of crops and livestock) and deforestation.

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Positively with energy use total and via coal, oil products, natural gas. Although coal use accounted for about 61 percent of CO2 emissions from the sector, it represented only 24 percent of the electricity generated in the United States in 2019. Natural gas use accounted for 37 percent of electricity generation in 2019, and petroleum use accounted for less than one percent.

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Negatively with nuclear energy supply since it is zero-emission clean energy source. Despite producing massive amounts of carbon-free power, nuclear energy produces more electricity on less land than any other clean-air source. Nuclear energy produces minimal waste.

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Positively and highly significant with energy supply from coal and natural gas. This indicates that CO2 emissions per capita is impacted significantly by preference for energy production through coal and natural gas combustion against crude combustion, which is used primarily as transport fuel and chemical production.

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Negatively by country’s new technology learning ability (assessed through high technology exports). This is also found as one of the critical influence factors with similar relation with emissions by Zhao et al.

The participation of renewable sources of energy as % in total energy supply is impacted –

(Refer-Table 2-For DV2 i.e renewable energy production (RE in % of total energy supply) results from EViews).

Table 2

For DV2 i.e renewable energy production (RE in % of total energy supply) results from EViews

DV2-renewable energy production (RE in % of total energy supply)
Hypothesis/IDVs	IDV	Coefficient	$t$ -stat	$p$ -value	Relation with DV	Coefficient
Control factors	GDP (current US$)				**	Positive
	Population density (people per sq. km of land area)				NS	Positive
	Urban population growth (annual %)				***	Negative
	Tax revenue (% of GDP)				**	Positive
H1	Electric power consumption (kWh per capita)	$-$ 0.003	$-$ 4.705	0.000	***	Negative
H2	Surface area (sq. km)	0.000	0.717	0.474	NS	Positive
H3	Industry (including construction), value added (% of GDP)	0.021	0.237	0.813	NS	Positive
H4	High-technology exports (% of manufactured exports)	1.505	23.766	0.000	***	Positive
H5	Crude Oil Prices, WTI, $/bbl	0.167	2.314	0.021	*	Positive
H6	Coal price, (Northwest Europe marker)	0.021	1.320	0.188	NS	Positive
H7	Natural Gas prices, OECD. USD per MMBTU	$-$ 1.011	$-$ 2.775	0.006	**	Negative
H8	Coal, peat and oil shale, Imports (PJ)	0.001	3.227	0.001	**	Positive
H9	Coal, peat and oil shale, Exports (PJ)	$-$ 0.001	$-$ 3.700	0.000	***	Negative
H10	Coal, peat and oil shale, Total energy supply (PJ)	0.000	$-$ 1.599	0.111	NS	Negative
H11	Coal, peat and oil shale, Total final consumption (PJ)	0.000	$-$ 0.140	0.889	NS	Negative
H12	Crude, NGL and feedstocks, Imports (PJ)	0.000	$-$ 0.427	0.670	NS	Negative
H13	Crude, NGL and feedstocks, Exports (PJ)	$-$ 0.003	$-$ 8.543	0.000	***	Negative
H14	Crude, NGL and feedstocks, Total energy supply (PJ)	$-$ 0.004	$-$ 3.261	0.001	**	Negative
H15	Crude, NGL and feedstocks, Total final consumption (PJ)	0.014	1.038	0.300	NS	Positive
H16	Oil products, Total energy supply (PJ)	$-$ 0.005	$-$ 4.142	0.000	***	Negative
H17	Oil products, Total final consumption (PJ)	0.003	2.778	0.006	**	Positive
H18	Natural gas, Imports (PJ)	0.002	2.550	0.011	*	Positive
H19	Natural gas, Exports (PJ)	0.006	10.771	0.000	***	Positive
H20	Natural gas, Total energy supply (PJ)	$-$ 0.003	$-$ 3.047	0.003	**	Negative
H21	Natural gas, Total final consumption (PJ)	0.004	3.954	0.000	***	Positive
H22	Electricity, Total energy supply (PJ)	0.034	7.905	0.000	***	Positive
H23	Electricity, Total final consumption (PJ)	0.001	1.490	0.137	NS	Positive
H24	GHG emissions from energy	0.106	21.204	0.000	***	Positive
H25	GHG Fugitive emissions	0.000	$-$ 3.932	0.000	***	Negative
H26	GHG emissions from fuel combustion	$-$ 0.109	$-$ 22.492	0.000	***	Negative
H27	CO2 emissions/population	0.714	2.318	0.021	*	Positive
H28	Energy efficiency	$-$ 0.008	$-$ 3.097	0.002	**	Negative
H29	Fossil fuels	$-$ 0.002	$-$ 0.950	0.343	NS	Positive
H30	Renewables	0.011	4.466	0.000	***	Positive
H31	Nuclear	$-$ 0.001	$-$ 0.801	0.424	NS	Negative
H32	Hydrogen and fuel cells	0.006	0.955	0.340	NS	Positive
H33	Other power and storage technologies	0.006	0.974	0.331	NS	Positive
H34	Other cross-cutting technologies/research	0.004	2.201	0.028	*	Positive
Color code implies				Significant		Negative
Color code implies				Significant		Positive
	Very high significance	***
	High significance	**
	Low significance	*
	Not significant	NS

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Positively with import dependence on coal, peat, shale gas and natural gas. These economies are pushed and motivated to shift towards RE as their economies are suffering due to this import burdens. This relation with dependence on import is contrary to the findings of Popp et al.

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Negatively with export volumes of coal, crude and NGL. This indicates that countries which are rich in fossil fuel sources of energy are reluctant to shift towards RE adoption as energy mix. They might be venturing into technology development but not as their source of energy production.

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Positively by economic factors like country’s technology learning ability (assessed through high technology exports volume as % of GDP) and increasing electricity supply (which is indicator of increasing national capacity of energy production). This indicates that on national scale – new technologies and capabilities are focused towards RE adoption.

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Positively by R&D investments in renewable energy technology development, cross cutting technologies and negatively with investments in fuels’ energy efficiency technology advancements. This indicates that future technologies and hence businesses will be focused for lower carbon emission (by increased energy efficiency) and RE technology integration in transport and energy production.

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R&D investments in hydrogen and fuel cells, storage have not significant relation since significant investments in these areas are done for less than a decade and their impact is yet to be observed on the energy sector. Storing excess renewable energy as hydrogen fuel is woefully inefficient compared to storing renewable electricity as, well, electricity. The repair and maintenance costs of fuel cells (due to low reliability) can lead to a substantial cost increase and decrease in availability, which are the major factors for end-user acceptance. The fuel cell industry must face the challenge of how to overcome this reliability barrier.

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Impact from industry, surface area and total electricity consumption was found not significant. This might be due to the dominance of other variables making these factors not so relevant.

6. Conclusion

Based on extensive literature survey conducted, this study identified many critical and influencing variables to most of the prominent questions today of rising carbon dioxide emissions and need to increase the contribution of renewable energy in total energy mix. The results reveal that import dependence on energy sources and increased electricity demand are most significant factors towards renewable energy adoption. At society level, agriculture has always been seen as the fundamental activity and major source of employment in development economies, but this has very significant contribution to carbon dioxide emissions. This is a key message for society and policymakers to take note of and work towards pivoting the economies to other sectors while reducing emissions. Coal thermal power generation has been one of the significant emission sources as identified in this study. Business enterprises and state-run power houses shall take a note that increased focus on carbon emission reduction will push them towards increased taxes or penalties. Hence, they shall start to work towards adopting and establishing renewable sources for energy demands for their enterprises. The impact of R&D investment is not very significant as some might argue. Therefore, there is a need to ramp up the spending on future technology innovations for faster adoption with reduced commercialization costs.

This study is on recent 30 years (1990–2019) timeseries of data while evaluating multitude of technical, economic and social parameters. Some of the other studies have worked on smaller or very large country set, whereas this was restricted to 15 largest economies today. The relation of parameters including energy source import, export, supply and consumption all in same set is novel in this work.

Footnotes

Acknowledgments

I am grateful to the MDI faculty, community and all its members’ staff for their considerate guidance, and to all the participants from academia and industry in energy sector who gave their valuable time for interviews for this study and enabled this research to be conducted.

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