The Role of Raw Material Prices in Renewable Energy Diffusion

The foremost need for countries especially Pakistan and other Asian countries is the hunt for renewable and alternative energy because the scarcity of energy is faced by many countries like Pakistan. There are a number of approaches seeking to demonstrate the diffusion of renewable energy technologies (RET). Learning and experience curves are the highest generally exercised instruments, followed by further economic, policy- and barrier-related analyses. The purpose of this study is to support research on the role of raw material prices specically oil price and gas price on Renewable energy diffusion. Panel regression with xed effects model is applied on data of 3 Asian countries namely Pakistan, India, and China for the period 1990 to 2012 gathered from World Bank and EIA (Energy Information Administration). The ndings reveal that the model conrms an adequate t and a very signicant inuence of oil prices on investments in renewable energy technology capacity whereas the insignicant inuence of natural gas prices in a renewable energy technology capacity. response time of in RET on raw material price changes. Here prices of raw material include crude oil price and natural gas price.


Introduction
The progress of international energy seems to be apparent with respect to their particular features in future years despite some necessary reservations.
The growth in the world economy, population, and a nonstop change in the focus of energy consumption to emerging Asia and other economies result in an ongoing upsurge in energy services demand (OECD/IEA, 2015). Now the foremost need for countries especially Pakistan is the hunt for renewable and alternative energy because the scarcity of energy is faced by many countries like Pakistan also and therefore it is severely touching economic growth.
Renewable energy has a minor portion of the overall energy mix. Cost demerit is a general assessment regarding the generation of renewable energy. But to achieve grid parity, the growing prices of fossil fuel in the Islamic Republic of Pakistan and the su cient accessibility of renewable energy (RE) might facilitate (Awan & Khan, 2014).
The present reserves of oil are about to remain for an additional 40 years only if existing fossil fuels are consumed at a similar percentage. In the case of Gas, these are about to remain for an additional 65 years. The present reserves of coal are about to remain for an additional 155 years as suggested by Bionomic fuel. A lot of coal will be consumed with alternative energy sources if reserves of oil and gas are used nonstop at a constant rate (Iwaro & Mwasha, 2010).
Wasteful energy deployment, the absence of public knowledge, and unprogressive infrastructure are the main problems faced by Pakistan with respect to its requirements for energy. For the successful conversion of energy into renewable, it is needed that extensive investment and strategic support took place in addition to the program and projects of the government that are in progress.
To attain its energy requirements through renewable, satisfactory targets are set by Pakistan in their strategies. Also, Pakistan spends 3 Billion US dollars each year to bring in oil from other countries being a poor country and 1% is the annual increment ratio in this spending (Shah et. al, 2011).
The alternatives of technology attainable to decrease emissions of carbon in the energy sector also comprise renewable energy sources (RES) that are not all the time but normally free of carbon. But the cost of these technologies is exceeding the cost of conventional fossil fuels (Popp, Hascic & Medhi, 2011).
Somewhat rapid and unpredictable changes in prices of raw material markets prove the dependency of a large number of technologies on prices of raw material mainly in recent years (Kehrel & Sick, 2015).
Over the earlier few decades, the national development agenda of many of the established countries includes the deployment of renewables. The emphasis is largely on the production then "crises of energy" in the 1980s plan consequently showing the volatile characteristics of oil prices in those days. So, the emerging viable substitutes for traditional fuels are renewables. In the years the 1990s, maintainable development, establishing the role of worldwide action expected at deal with climate change consequently connected the renewable energy sources (RES). In the last few years, renewable energy sources (RES) deployment has faced an outstanding worldwide growth pro le, supported by this rising signi cance (Aguirre & Ibikunle, 2014).
Throughout the most recent decade although, an extensive rise in the amount and in uence of ups and downs in crude oil and natural gas prices can be witnessed. With regard to prices of raw material, this research would like to highlight, that from the perspective of this study, since prices are for manufacturing rms of renewable energy technology tools, they are measured to be externally programmed. Due to that, the emphasis at this point is xed entirely on price history, putting away the reasons (circumstances of demand and supply, assumptions, etc.) that could be at the core of these changes.

Problem Statement
To describe facts and hurdles of RET diffusion, scholars began studying the renewable energy diffusion (RET) diffusion in the later years of 1990, and up to now, various techniques have been reported by a number of researchers. In literature, renewable energy diffusion obstacles, strategy attributes, experience and learning outcomes, and integrated system models are the highly investigated characteristics. But the impact of prices of raw materials speci cally oil and gas prices on the diffusion of renewable energy technology (RET) is one new viewpoint in this domain. Every part otherwise being equivalent, the prices have been expected to continue unchanging up till now (Kehrel & Sick, 2015).
The general purpose is the diffusion of RETs and to accomplish it, the key emphasis of the research eventually is to offer paths to organize all elements happening independently to come together and function as a separate entity (Shah et al. 2011).
Since the market of energy is fundamentally a commodity industry, prices act for the toughest benchmark in assigning shares of the market. Regardless of the type of raw materials that are required for the production of Renewable energy technology, the energy product prices speci cally crude oil and natural gas act as important parts while evaluating the diffusion of renewable energy technology. Although prices of raw material have each time appealed concentration in the scienti c society and are presently a topic of discussion in the academic world alongside in industry.
As in my knowledge, there is no study exclusively on the relationship between RET diffusion and Raw material prices especially in Pakistan and Asian countries presently. So this research would contribute to the study on Renewable energy diffusion (RET) by adding up the prospect of prices of raw materials includes Crude Oil price and Natural Gas price.

Research Question
This study is carried out to discover answers to questions as mentioned below. Renewable Energy (RE) is originated through natural procedures that are replaced repetitively. Primarily or secondarily, produced renewable energy in its several shapes from the sun and heat within the earth. Renewable energy includes energy produced from solar, biofuels, geothermal, wind, and hydro and ocean resources.
In this research, the investment in RET per capita for country and time described the diffusion of renewable energy technology (RET). First, the renewable energy installed capacity is converted from GW to KW. Then, by way of dividing net capacity in KW by population, the Net Investment will be the result.

Raw Material Prices (RMP)
In this piece of research, the prices of raw material are at rst denoted by a 1-year gap because of a particular response time of investments in RET on raw material price changes. Here prices of raw material include crude oil price and natural gas price.

Crude Oil Price.
A few of the very main commodities for the worldwide economy is Crude Oil. To determine the spot price of the various barrels of oil, the crude oil price is applied. There are a number of factors that in uence the demand and supply of crude oil on which crude oil prices depend from which several products like gasoline are obtained. Home cooking oil, gasoline, electric power production, and manufacturing are directly affected with respect to their cost by the higher crude oil prices.
For the data analysis, Europe Brent Spot Price FOB (Dollars per Barrel) on an annual basis has been taken in this research. Natural Gas Price.
In order to encounter the growing need for energy in the country, the Government is spending its energies on improving the production of gas. Demand and supply develop the prices of natural gas primarily like with other commodity prices. Yet, the crude oil price and petroleum products also develop natural gas prices. The prices of natural gas traditionally followed oil prices, but in the last few years, natural gas prices are changing to some degree with coal prices instead of Oil prices. In this study, natural gas prices in terms of US dollar per MMBTU (million British Thermal Units).

LITERATURE REVIEW
In this study, the diffusion of Renewable Energy Technology (RET) is primarily built on environmental and matters of energy security. To build Renewable energy technology (RET) further competitive in comparison with reputable technologies of fossil fuel, the approaches of learning and experience curve place in uence on the necessary decreases in Renewable energy technology (RET) cost. It will demand excessive nancings or subsidies to make RET economical, although Renewable Energy Technology (RET) has a greater pro ciency for cost reduction as determined by a number of approaches.
The concept of the learning and experience curve is built on the thought that as the experience is obtained by the worker; the expected time to accomplish a task reduces. The main idea is that as the aggregate production double up, the time or cost of accomplishing a task (e.g. manufacturing a production unit) lowers. This connection was perhaps rst determined in the year 1936 at Wright-Patterson Air Force Base in the United States (US). Rao and Kishore (2010) stated that the diffusion of renewable energy technology (RET) involved adaptation barriers of renewable energy technology and techno-economic, learning, and experience curve approaches. They applied the learning and experience curve to examine the diffusion methods of Renewable Energy Technologies (RETs). They discussed that technological advancement linked along with technology. For example, the enhancements made by experimentation, execution and research and development (R&D) during the production activity is controlled by social and economic policies together with economic potentials as indicated by the learning curve concept.
With the purpose of taking into attention that Renewable Energy Technology (RET) because of their excellent capability for cutbacks in cost, results in an excessive rate of learning and fewer inclined to environmental taxation are supposed to effectively challenge the traditional technologies in coming time (therefore working such as backstop technologies), thus, (Kumbaroglu, Madlener, and Demirel, 2008) make use of learning curves for Renewable Energy Technologies (RETs). Research and development (R&D) expense in uence on decreases in costs is also involved whereas Latest methodological developments with regard to learning curves applied two-factor models, wherever as well as learning-by-using. Harijan, et.al, (2011) stated that an S-Curve as presented in Fig. 1 describes the diffusion of technology over a period of time. S-curve is a mix of slow initial growth, after that rapid growth subsequent to a de nite take-off point and then once again a slow growth on the way to a nite upper limit to the distribution.
Empirical Review Ata (2016) engages panel data for the time span of 1990 to 2008 to perform econometric analysis of four types of renewable energy (RE) policies i.e. feed-in-tariffs (FITs), tenders, quotas and incentives in tax in supporting the deployment of renewable energy technologies (RETs) in 27 nations of European Union (EU) and 50 status of United States (US). A xed-effects regression model and primarily panel data set with instruments of policy and further factors around nationwide or state-level are used. The results present that tax and feed-in-tariffs (FITs) both positively and signi cantly in uence the potential of deployment of renewable energy (RE) in both the US and Europe. The quota has an insigni cant impact on renewable energy deployment. However, incentives in tax and tender positively impact renewable energy (RE) deployment. Also, renewable energy deployment is signi cantly affected by electricity usage, nuclear energy, and prices of coal while income, prices of gas, security elements, and CO2 emissions are insigni cant in the deployment of renewable energy.
For the period between 1997 and 2014, Biresselioglu, Kilinc, Onater-Isberk, and Yelkenci (2016) in their study examines the in uence of factors related to environmental, political, and economic on the development of installed wind capacity all over the world with consideration of various regions. The variables employed for this research are the development of installed wind capacity, GDP (Gross Domestic Product) per capita, the dependence of total energy import, formation of carbon dioxide (CO2) emission, stock of foreign direct investment (FDI), the potency of primary energy, and the apportions of wind and hydroelectricity utilization in the production of electricity and electricity price. System GMM (System Generalized Method of Moments) program is executed to disclose dynamic relationship on the variables in the model. Including non-continental & Southern Europe, Northern Europe, Western & Central Europe, and non-European OECD (The Organization for Economic Cooperation and Development), the approximations for the phase 1997 to 2014 are narrated for the experiment of 26 nations, along with dissimilar territories. For the estimated in uences, a group of prior hypotheses is further investigated. The ndings disclose that the capacity growth of installed wind is in uenced by various indicators that con rm prior hypotheses of this research. Also, it was found that in the prior phase, the greater capacity of installed wind has a positive in uence as compared to the present phase. Similarly, the capacity growth of installed wind capacity is also in uenced by greater carbon dioxide (CO2) discharges. On the other hand, dissimilar states encounter different in uences on many variables, for example, electricity price and entire import dependence. Ma, Chen and Li (2013) investigate the renewable energy (RE) usage rate and their procedures throughout the growth of the renewable energy (RE) market which is in uenced by market need, policy encouragement, and technological development. In order to investigate the in uence of the market, policy, and technology on the usage rate of renewable energy (RE), a panel data set of 28 nations of OECD (The Organization for Economic Cooperation and Development) for a total of 34 years is built. The ndings of their research reveal that the price of oil is slightly statistically signi cant merely in the whole set, plus the coe cients' sign is positive which is coherent by the argument that renewable energy (RE) is an alternative for fossil energy Also, GDP (log) per capita and the level of imported energy's dependency, are expected to have a positive link to the renewable energy's usage rate. In all 28 OECD nations' regression, the indication of the coe cient is certain with the expectation however statistical impact of those coe cients has a big difference. Romano and Scandurra (2016) discover the prevailing differences in the main elements in uencing investments in two distinctive production sources of renewable energy (RE). In their study the researchers suggested a dynamic panel analysis of investment in renewable energy for a sample of 32 countries includes countries of OECD (The Organization for Economic Cooperation and Development) and Brazil, China, India, South Africa, and Russia) with dissimilar social and economic compositions for the years concerning 2000 and 2008. As proved in the result the main aspects encouraging Renewable energy sources investments differ as per generation basis measured. The policies are proving to be an in uencing factor for renewable energy (RE) investment. Progress in the conditions of environment performed through investing in sources of hydroelectric however further sources found insigni cant. The ndings further indicate that the thermal and nuclear electricity share slow down renewable energy (RE) investment.
Zam r, Colesca and Corbos (2016) intended to analyze the public related policies employed to promote renewable energy (RE) growth in Romania for the period of past 10 years. They examine Romania's renewable energy (RE) sector with regards to economic and analytical ability, generation, and utilization of renewable energy (RE) and regulation associated with renewable energy (RE). The nding state that development of renewable energy (RE) is backed by public policies Marques and Fuinhas (2012)  supporters with regard to the deployment of renewable energy. But the outcomes present that policies regarding incentive and subsidy like feed-intariffs (FITs) and policy processes like strategic arrangement act as a main part in the deployment of renewable energy (RE) in European countries.
Salim and Ra q (2012) recognize the factors of renewable energy spending in six prominent renewable energy nancier evolving countries by using panel data together with time series econometric techniques. In the long-term, income and emission of pollution in China, Brazil, Indonesia, and India signi cantly affect the consumption of renewable energy. If talk about Turkey and the Philippines, this study found income as a major driver for the consumption of renewable energy. With reference to each of these countries, the spending on renewable energy is negatively and less signi cantly affected by oil prices. The elasticity indicated from both panel methods is observed to be reasonably stable for fully modi ed OLS (FMOLS) and Dynamic OLS (DOLS) and the Autoregressive distributed lag (ARDL).
In the study conducted by Gan and Smith (2011) among The Organization for Economic Cooperation and Development (OECD) nations, nd key drivers that support the promotion of renewable energy technology and bioenergy. By panel data modeling, their results disclose that renewable energy supply per capita and bioenergy in OECD states are signi cantly affected by market consumption of alternative energy and bioenergy and Gross domestic product (GDP). Along with these regular motivating in uences, there were a few additional particular country-level elements also that impact renewable energy and bioenergy progress in single countries. Alternative energy and bioenergy, innovation-related policies, market-related energy policies that offer incentives else wise enforce taxes to moderate the market and environmental exterior, and prices of energy, CO2 discharges, and government have a mathematically insigni cant in uence of these elements on the dependent variable. Popp et al. (2011) applying the dataset of PATSTAT including 26 nations of The Organization for Economic Cooperation and Development (OECD) consider investing in solar photovoltaic (Solar PV), geothermal, and electricity from biomass, wind, and waste for the time span from 1991 till 2004.
They measure the consequence of increased intelligence on renewable energy investment. The ndings show that knowledge stock has a signi cant impact on investing in Energy capacity per capita means the gain in knowledge growths the investment in renewable energy. Shrimali and Kniefel (2011)

Research Method
Climate variation has been challenging in current centuries as CO2 discharges keep on rising in established and developing nations. There is an opportunity for evolution concerning a society (that is less carbon) is to enlarge the energy share especially electrical energy produced through the sources of renewable energy (RE).In recent time, investments in clean energy plans have been rising and entire t for an appropriate extent. So far the investments in traditional fossil-based fuel power are higher than the volumes of renewable energy technologies (RETs) (Polzin et al. 2015).
As the energy sector is ultimately an industry of commodity and the prices indicate the utmost yardstick in distributing shares in the market Lund(2007). Crude oil and natural gas that represent energy commodities prices perform a major part in investigating the diffusion of renewable energy technologies (RETs), regardless of the type of raw materials that are required for renewable energy technology (RET) production.

Research Model
We used the below model established by Johnstone, Hascic and Popp (2010) and Popp et al. (2011) with the aim of discovering empirical proof for the role of oil and gas prices on renewable energy technology (RET) diffusion, which is applied to this study framework and broadened to a factor of raw material price: Research Model RET i, t = β 1 + β 2 RMP_OIL t-1 + β 3 RMP_GAS t-1 + β 4 GDP i, t + β 5 GROWELEC i.t-1 + β 6 COAL i, t + β 7 NUCLEAR i, t + I, t

Research Hypotheses
The purpose of this research is to describe the diffusion of renewable energy technologies (RET). The raw material price like oil and gas prices plays a role in describing this diffusion. So in order to de ne the diffusion of RET, the below hypotheses have been built.

Research Design
To conduct this research, a quantitative research approach is applied. The data is gathered from the secondary source and this is explanatory research.

Data Source
The secondary sources of data collection are World Bank, Datamarket.com and IEA (International Energy Agency) that covers 23 years (1990 -2012). Raw material prices (RMP) are denoted by a year gap due to some response time of renewable energy technology (RET) investments on raw material price changes.

Population
Page 7/10 The projects in Pakistan, India, and China that contribute in the diffusion of solar, wind, and all renewable energy sources are examined.

Sample Period
The sample size is69 as data covers 23 years (1990 -2012) for 3 countries. The data has been collected for all variables from 1990 because there is no such boom of renewable energy before this period. And the data has been collected till 2012 as renewable energy installed capacity data is not available after 2012. Pakistan, India and China have been selected as previously the related research has been conducted for European and other OECD countries. So, in this research, Asian top countries i.e. Pakistan, India, and China on the basis of their energy use has been selected Statistical Technique To analyze the role of oil prices along with gas prices on Renewable Energy Technology diffusion, the Panel regression model is used.    Table 3 explains the regression analysis for the dependent variable which is RET on various independent variables such as Coal, GDP, Growth in electricity, Nuclear, Gas, and Oil. The ndings explained the insigni cant relationship of Coal, Growth in electricity, and Gas with RET which also rejected the hypothesis that there is a signi cant relationship of Gas with RET whereas Oil has a positive signi cant relationship with RET similarly, and accept the hypothesis Oil has a signi cant relationship with RET similarly GDP and Nuclear has a negative signi cant relationship with RET. R squared is 0.68 which indicated that 1-time change occurred in RET due to 0.68 times of independent variables.

Conclusion
In the context of this contribution, we have studied a model for demonstrating the in uence of raw material prices on RET diffusion. As a rst step, we tested the explanatory power of crude oil and natural gas prices for Renewable energy technology. The model exhibits an adequate t and a highly signi cant in uence of oil price on investment in RET capacity as measures of diffusion. A strong model t is observed. Electricity production from coal sources also found to be an in uencing factor in the diffusion of RET.
In contrast, Natural gas price which is among the main independent variable in this model is non-signi cant as also con rmed by Ata (2015). GDP (per capita growth) also proves to be non-signi cant factors on the diffusion of RET in the perspective of Pakistan, India and China. GDP per capita growth indicates that Renewable energy technology diffusion is not essentially associated with the well-being of economy as also examined by past researchers. Growth of electricity consumption and electricity production from nuclear sources also found to be non-in uencing factors on the diffusion of RET. Johnstone et al. (2010) and Popp et al. (2011) also authenticate the result that the electricity demand or energy demand at large is not a key reason for the diffusion of RET. Similarly, Nuclear power does not have many effects on RET diffusion, which may be due to the low percentage of Nuclear power share in electricity production in Pakistan, India and China.

Recommendations
In the light of the above analysis and interpretations, it is suggested that 1. When companies do planning or forecasting related to renewable energy, they should also consider changing the prices of oil and gas.
2. However, it is found that Oil price impact RET diffusion, organized and e cient incorporation of raw material price changes into devising and projecting procedures of companies can be practiced. 3. O cials from the energy sector and interrelated industries should evaluate the effect of raw material price uctuations on energy market evolutions. 4. As electricity production costs emerge to perform a key role for the examination of diffusion of RET, they should be incorporated in the model as an alternative of non-signi cant variables like the growth of electricity consumption. Figure 1 Diffusion Curve