Quantile analysis of carbon emissions in China metallurgy industry

Journal Pre-proof Quantile analysis of Carbon emissions in China metallurgy industry.

Nelson I. Benjamin, Boqiang Lin PII:

S0959-6526(19)33404-3

DOI:

https://doi.org/10.1016/j.jclepro.2019.118534

Reference:

JCLP 118534

To appear in:

Journal of Cleaner Production

Received Date:

11 March 2018

Accepted Date:

20 September 2019

Please cite this article as: Nelson I. Benjamin, Boqiang Lin, Quantile analysis of Carbon emissions in China metallurgy industry., Journal of Cleaner Production (2019), https://doi.org/10.1016/j.jclepro. 2019.118534

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Quantile analysis of Carbon emissions in China metallurgy industry. Nelson I. Benjamin a, Boqiang Lin b a School

of Business, Nanjing University of Information Science & Technology, Nanjing,

Jiangsu, 210044, PR China. Email address: [email protected] b School

of Management, China Institute for Studies in Energy Policy, Collaborative Innovation

Center for Energy Economics and Energy Policy, Xiamen University, Fujian, 361005, China. E-mail addresses: [email protected]

Abstract The metallurgy industry that was crucial in developing Chinese economy also enhanced energy consumption and carbon emissions. This paper examines the impact of economic variables on carbon emissions from the metallurgy industry of China, where energy structure, energy intensity, carbon intensity, industrial structure, and labor productivity were utilized in analyzing carbon dioxide emissions within a quantile models framework. Quantile estimates unveiled varying effects of variables across spectrum of carbon emissions and averagely, a unit increase in the above economic variables will influence carbon emissions by 97.2 percent, 100.3 percent, 118.6 percent, 98.4 percent and 100.2 percent approximately. Across all quantiles, results showed that carbon intensity had the greatest impact on carbon dioxide emissions, then energy intensity, labor productivity, industrial structure, and energy structure, most but not all the industry was plagued by carbon intensity, while industrial scale should be minimized optimally, labor productivity should be improved too. Energy intensity is the most influencing

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factor, prompting an urgent need for technology advancement The uniqueness of the metallurgy industry must be considered when administering economic policies across the industry in China. The enhancement of clean energy technology and optimizing energy structure are crucial for carbon emissions reduction and a review of energy consumption by the industry to accommodate renewable energy is recommended. Key words: Metallurgy industry; Carbon dioxide emissions; Quantile estimates. 1. Introduction The industrial sector is important to an economy because of its indispensable role in various forms, such as: the modernization of agriculture by supplying fertilizers, pesticides etc, tractors, pump sets, threshers and harvesters to increase productivity; encourages the development of science and technology; helps in capital formation; a bedrock for urbanization and sprouting of ultra modern cities; promotion of trade; and poverty alleviation through employment etc. Suffice to say, industry is the nucleus for economic development, and economies of scale can be attained by the application of advanced technology and division of labor, alongside scientific management in order for production and employment to increase rapidly and boost economic growth and standard of living. China is a large market economy both in terms of population and total economic product, and is arguably the manufacturing hub of the world. Core industries are manufacturing, agriculture and telecommunication services where in 2015, the Chinese economy was an economic powerhouse compared to fifty years ago when China was a struggling economy characterized by poverty, hunger, and depression (Lin and Nelson, 2019). China has a diverse spread of industrial production which dominates the economy despite a 3.3 percent drop in GDP, industries still accounted for 45.3 percent of total GDP in

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2012 (economy watch, 2012), and major industries includes: mining and ore processing, steel, aluminum, iron and other metals, coal; armaments; textiles; machine building; petroleum; chemicals; fertilizers; cement; and also consumer products which includes: toys, electronics and footwear; food processing; telecommunications equipment, satellites and space launch vehicles; transportation equipment which includes automobiles, locomotives, aircraft, ships and rail cars. Nevertheless, as the Chinese economy slowed down, the dominance of the industrial sector is waning and share of services in total GDP are rising as government unveils plans to tackle socio economic cost of environmental pollution due to industrial revolution. The planning objectives of the central government is to revamp all industries across China to be technologically advanced by adopting a high tech and low carbon emissions strategies, and allocating better resources to innovation and technology in order to limit the impact of China's heavy industry and ensure a green and sustainable economic growth. The attainment of these goals among other factors must depend on new economic models, that will consistently reveal progress along this pathways, if possible, better than traditional estimation techniques. It is debatable that the tremendous economic leap of China had in combination environmental hazards like emissions, resource depletion, and degradation, where heavy industries (metallurgy industry) was a major culprit that significantly increased carbon dioxide emissions, thereby making China number one emitter of carbon globally (Lin and Nelson, 2019). Figure 1 depicts the incessant increase of carbon emissions and in 2006, China replaced the US and became first in global emissions table where in 2014, its carbon emissions amounted to 27.50 percent of global total emissions. Former economic procedures were usually the transference of high polluting industries from an economic viable region to a less economic region, this trend garnered attention after the 2008 economic crises, whereby both polluting industries and

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hazardous waste industries recorded a high level of transfer and relocation across China. However, several studies showed that this relocation had only a short term positive effect on economic growth, in the long-run, negative externalities of pollution had a negative significant impact on the economic growth for the receiving regions. This economic development strategy is flawed and should be replaced because pollution is not curbed, but rather directed to other areas and the growth of carbon dioxide emissions usually continues unabated. To efficiently account for carbon emissions from this industry, new econometric techniques should be employed in strategizing and analyzing economic variables around emissions. This research will provide new insights geared towards carbon emissions, by the careful partitioning of China into regions using median based models, and studying emissions through these subsamples. This approach will reveal core variables that influences emissions by regions because different regions with regards to economic growth and urbanization might have different economic variables that are emissions friendly. We also hope to widen the scope of literatures on factors contributing to carbon emissions in the industrial sector of the economy. Insert Figure 1 here The metallurgy industry of China is basically classified into two categories namely: ferrous and non ferrous metallurgy industry. The former comprises of iron and steel industry while the latter is further grouped into heavy, light, and thin metallurgy. The total output value of the metallurgy industry shows the importance of the industry because of its services to all sectors of the Chinese economy. As shown in figure 2, there was a continual increase on output values, which consumed a significant amount of inputs because large raw materials were needed to meet the insatiable demands on the industry both locally and internationally. The iron and steel industry had far greater share in the total output of China's metallurgy industry than the non-

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ferrous metal industry and in 2014, the total output by non-ferrous metal industry was roughly 5131.2billion and the total output by the iron and steel industry was 7433.3billion, amounting to 59.2 percent approximately in the share of metallurgy industry, which was a drop from the previous year where its total share was 60 percent. The scenario of the total output volumes by the industry demands an overview about its energy consumption, primary energy sources and energy mix. The total energy consumed by the industry is huge and continues to increase, especially by the iron and steel industry, large amount of energy is needed to enhance production as shown in figure 3. As a result, the industry is classified as a high emission and high polluting sector, and its primary energy mix are: natural gas, fuel oil, coking coal and power coal. As the industry continues to expand due to enormous demands and in order to procure profits, readily available energy sources are used that are cheap, high emitters of carbon dioxide and are also great influencers of China's carbon dioxide emissions. More than 20 percent of China's energy consumption is accrued to the metallurgy industry and in 2014, approximately 868.53 million tons of coal was consumed alone by the industry, resulting to a 20.4 percent of the total energy consumption of China. Insert Figure 2 here Insert figure 3 here Furthermore, the overall efficiency of China's metallurgy industry has a huge impact on the industrial development and welfare of the country (Lin and Du, 2017). In 2013, environmental pollution that accompanied China's economic leap became obvious, as residents made demands for healthier environments and pressured local governments to enforce environmental protection laws and regulations, with provisions for fine on defaulting industries.

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To tame social unrest due to pollution, main expectation in the 13th Five-Year Plan economic goal is emission reduction and the metallurgy industry due to its high energy consumption is a core sector for emission reduction policies, however, the efficacy of any policy geared towards this industry will depend on a careful analysis of all factors that influences its carbon emissions on a quantile basis, in order to fully grasp emission reduction policies because other studies analyzed these factors by employing an econometric techniques that are mean based. Motivation for this research is to ascertain and analyze the existence of any long-run relationship among variables and to visualize the impact each variable has on carbon emissions in different quantiles (percentiles). This research will also fill the void created by lack of analysis on emissions using quantile model in this industry and also unveil new econometric technique to study economic variables. The choice of quantile rather than other mean based models is because of the anomalies associated with mean based techniques, median analysis is robust to outliers and provides better description and characterization of dataset, and are more flexible in modeling heterogeneity. Some other methods of estimation lack the ability to handle endogeneity, however, quantile analysis is dynamic and can ascertain endogeneity. Therefore, utilizing data from the metallurgy industry of China within a quantile model premise, we investigate systematically, the dynamic impact of economic variables on the carbon dioxide emissions of the industry. The remainder of this paper is organized as follows: the second and third sections reviewed existing literatures and presented data used and their sources, the fourth and fifth sections presented research methodology within a quantile premise and provided all empirical outcomes from different quantiles (percentiles), while the sixth section was conclusion of the analysis and policy suggestions.

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2. Literature review Several econometric tools have been applied in the study of influencing factors on carbon dioxide emissions of the industrial sector for developed and developing countries, because of resource constraint and mounting environmental degradation that has led to global warming. For example, Zhao et al., (2017) used the Generalized Log Mean Divisia Index (GLMDI) in studying the mitigation of carbon dioxide emissions in the industrial sector of the US, where variables were decomposed into economic growth, energy price, energy intensity, energy mix, industrial structure, carbon factor and concluded that industrial structure and carbon factor were the major contributors of carbon dioxide emissions. Adopting only Log Mean Divisia Index (LMDI) method in analyzing carbon dioxide emissions for the industrial sector of Philippines, Sumabat et al., (2016) studied the impact of economic variables like economic growth, energy intensity and energy structure on carbon dioxide mitigation, and concluded that the reduction of energy intensity and optimizing energy structures are needed for mitigating carbon dioxide emissions, while economic growth led to increase of carbon dioxide emissions. Though these methodology were economically feasible, using a mean based model in analyzing sensitive topic as carbon dioxide emissions might proffer conflicting solutions because very large or very small numbers can distort overall outcome. The input-output model is another approach that has been extensively employed in analyzing carbon dioxide emissions where Lin and Xie, (2016) investigated the main drivers of carbon dioxide emissions in the food industry of China, and concluded that energy intensity was crucial for potential carbon emissions reduction while total output effect generally led to increment in carbon dioxide emissions. Kucukvar et al., (2014) analyzed economic growth and industrial development associated with carbon dioxide emissions in industry across the US, and found that economic growth instigates growth in carbon

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dioxide emissions, while service sector development reduces carbon emissions. Also, Chen et al., (2016) analyzed the urban carbon transformation in Australia based on a multi regional inputoutput method, and concluded that mining activities lead to increase in carbon dioxide emissions, while import trade reduces carbon emissions, and suggested prioritizing the emissions reduction technology R&D of the mining industry. Other methodology includes: Decomposition analysis where Zhang et al., (2017) analyzed the impact of R&D investment and energy intensity on carbon dioxide emissions in the industrial sector of China and concluded that investment was the main factor driving carbon dioxide emissions growth, while energy intensity and R&D were main factors for emission reduction. Laspeyres decomposition method where Sun et al., (2017) estimated the main driving factors of carbon dioxide emissions in the power sector of China, positing that economic growth, energy intensity and electricity intensity were main influencers of carbon dioxide emissions. Markov switching technique where Rahman et al., (2017) analyzed the impact of economic growth, energy intensity and energy structure on carbon dioxide emissions from the industrial sector of Malaysia, and found that the optimization of energy structure, enhancement of clean energy technology were crucial for carbon emissions reduction and expanding energy consumption. System optimization models where Hasanbeigi et al., Chen et al., (2016) analyzed carbon dioxide emissions for the manufacturing industry and Dalian industrial sector of China, and concluded that the reduction potential for carbon emissions was 201.23 million tons for the manufacturing sector, while optimizing the industrial structure in Dalian would reduce carbon dioxide emissions and economic growth was a major accelerator for carbon dioxide emissions. Although many research has been performed on the main driving forces of carbon dioxide emissions in the industrial sector, the existence of anomalies in different estimation

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methods should not be overlooked, some traditional models are mean based inferences and in most cases, excessive number of economic variables for estimation decreases result accuracy. A median based model is better and not rigidly defined, thus dynamic by default. As succinctly presented by Guerron et al., (2017), most economic variables are dynamic in the nature of their relationships, hence some traditional method of estimation lack the prowess to handle endogeneity in economic data. Quantile analysis however is dynamic and can sufficiently handle endogeneity in data optimally. This method has been previously applied in energy literature to study energy prices and carbon dioxide emission allowance price (Shawkat et al., 2014), and residential energy consumption (Nikhil Kaza, 2010). Recently, it has been applied to uncover salient discrepancies and failures from other models such as in Lin and Nelson, (2019) and Li and Lin, (2015) where quantile estimates were able to account for outliers present in economic variables that resulted to a bias estimation, because median estimation is robust compared to mean estimates and revealed that on the average, a unit increase in economic growth, investment in R&D and energy price will result to 81.5%, 32.8% and 326.5% increase on the energy technology innovation of China, and inferred that the main driving forces for China's energy technology innovations was energy price in every province, then economic growth based on some less technologically advanced areas, and R&D investment based on some technologically advanced regions as against the traditional estimation methods that was employed in latter study where energy price was negative and insignificantly related to energy technology patents in China, which was against innovation and economic theories that clearly favors the idea that energy prices have positive impact on energy technology innovations. Also Lin and Nelson, (2017) on the analysis of influencing factors on carbon dioxide emissions from the transport sector of China, clearly reveal the impact of all economic variables on different quantiles of

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carbon emissions and found that the quantity of carbon dioxide emitted by the transport industry was approximately 1057.83Mt CO2 which was at the fiftieth quantile, while the highest and lowest was 1128.055Mt CO2 and 1022.84Mt CO2 respectively. These corresponded to the thirtieth and seventieth quantile and graphically captured the trend of carbon emissions, where there was an incremental trend in carbon emissions and concluded that more efforts are needed for the reduction of carbon emissions in the transport sector as against Lin and Xie, (2013) where the analysis wasn't detailed and was holistic in estimating economic variables. Thus, quantile analysis is used to clearly analyze main driving forces of carbon dioxide emissions from the metallurgy industry of China to see how far government policy on mitigating carbon emissions can be realized and if the result gives a positive outlook, then there is probable hope that targets on renewable in this sector can be attained in the near future as China continues on the path of green and sustainable economic development clearly outlined in the 12th five-year economic plan by the Chinese government for the fiscal years 2011 to 2015 where seven strategic industries were considered as high priority: biotechnology, information technology, new energy, environmental maintenance, new materials, high-end manufacturing and alternative fuels coupled with the large government investments being made into these areas. Our choice of quantile stems from the fact that against all other method of estimations, median analysis is more robust to outliers and embodies richer characterization and description of data coupled with its flexibility in modeling heterogeneous data distributions. There are several research on carbon emissions in this industry but none has really investigated carbon emissions quantile wise in addition to several anomalies associated with traditional models and estimation techniques, such as been negatively affected by outliers and are generally holistic in approach. Median analysis are more robust to outliers and embodies richer characterization and description of data, coupled

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with its flexibility in modeling heterogeneous data distributions. Quantile analysis will be used to clearly analyze the influence of energy structure, energy intensity, carbon intensity, industrial scale and labor productivity on carbon emissions for the metallurgy industry of China, where errors in data with be minimized and all outliers accounted for, in order to ascertain the effects of government policy on emissions reduction. A positive outlook on emissions reduction, could infer that targets on renewable in this sector can be attained as China continues on the path of green and sustainable economic development. 3. Description of dataset Core variables and provincial pollution indicators that facilitates carbon dioxide emissions in the metallurgy industry of China were selected primarily via two pathways: The first was adopting the Kaya identity pollution indicators, and the second was replacing some of the indicators due to the peculiar characteristics of the industry in China with regards to pollution emissions. Normally factors that have main influence on CO2 emissions in the metallurgy industry of China were divided into the following: GDP per capital, energy efficiency, carbon intensity and population, however our modified version for the Kaya identity will replace GDP per capita with industrial added value and population with employment, in order to study which of these factors have the most impact on carbon dioxide emissions in the metallurgy industry, so as to predict future mitigation potential. Indexes of energy structure, energy intensity, carbon intensity, industrial scale and labor productivity are chosen as the independent variables. Our variables are therefore labeled as ES, EI, CI, IS and LP and were calculated as shown in table 1. Insert table 1 here

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where 𝐸𝐹 is the total amount of fossil energy consumed, Y is the industrial added value, C is the total amount of carbon dioxide emissions and W is the number of employees in the metallurgy industry. As clearly shown in table 1, energy structure is simply the ratio of total amount of fossil energy consumption and the total energy consumption, energy intensity is the ratio of total energy consumption and total output, carbon intensity is the ratio of carbon dioxide emissions and fossil energy consumption, industrial scale is the number of workers with direct impact on energy consumption while labor productivity is the added value per unit of labor. The dataset include yearly observations of energy consumption of the metallurgy industry of China from 1991-2014, obtained from China Statistical Yearbook and number of employees and industrial added value data are from China Economic Database (CEIC). Data were formerly transformed into logarithmic form in order to improve data statistical properties. The total carbon dioxide emissions of the industry was calculated by multiplying every type of energy consumed in the industry by their carbon emission coefficient (obtained from Intergovernmental Panel on Climate Change) and then adding all types of the energy carbon emissions calculated. Electricity and coal are major energy consumed by the industry, accounting for more than 95 percent of total energy consumption and oil, thermal energy, natural gas having a lesser percentage share in total energy consumption. Figure 4 depicts the trend of carbon dioxide emissions from the industry and we verified an upward trend moving from one year to the other. In 2014, carbon emissions was roughly seven times more as it was in 1991 where roughly 1647 million tons of carbon dioxide was emitted as against 251 million tons in 1991, making the yearly increment of carbon dioxide emissions from the industry roughly an average of eight percent. The last 24 years has witnessed a leap in carbon emissions from the metallurgy industry of China, it is imperative to uncover major culprit and emission enhancing agents in

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order to administer a feasible mitigation policy for the industry as its proportion of emissions in the total emissions of China's carbon dioxide increased from approximately 10 percent to 17 percent nationally. Insert figure 4 4. Research methodology 4.1 Test for unit root Economic data were checked for stationary characteristics, stationarity was employed as a tool for transformation. Non-stationary behaviors such as trends, random walks, or cycles which are often present in variables make them unpredictable. Any estimate obtained by analyzing a non-stationary data will be spurious, thereby indicating a relationship between variables which may not exist. To get a consistent and reliable estimates, non-stationary data must be made stationary. Panel based unit root tests were considered because they perform better than unit root tests based on individual time series because of heterogeneity. A test on stationarity is a prerequisite for developing a quantile model of time varying variables. The unit root test is based on the following regression equation: 𝜌

△ 𝑙𝑛𝐶𝑂2𝑖𝑡 = 𝛼𝑖𝑡 + 𝜌𝑖𝑙𝑛𝐶𝑂2𝑖,𝑡 - 1 + ∑𝑗 𝑖= 1𝜑𝑖𝑗 △ 𝑙𝑛𝐶𝑂2𝑖,𝑡 - 𝑗 + 𝜖𝑖𝑡, 𝑖 = 1,…,𝑁 𝑎𝑛𝑑 𝑡 = 1,…,𝑇 (1) where 𝛼𝑖𝑡 = 𝛿0𝑖 + 𝛿1𝑖𝑡 is the deterministic component (constant and trend). 4.2 Quantile analysis A detailed analysis of carbon dioxide emissions in the metallurgy industry of China is indispensable for the realization of policies targeting national carbon emissions mitigation. Since

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China emerged as the largest emitter of carbon dioxide in the world with a record of 35 percent in global emissions, to ensure that policy targeting industrial emissions are achieved, standard mean based and other traditional estimation models are practically not suitable because of their inefficiency to outliers and holistic view. Quantile regression however, gives a detailed estimates of the effects of each explanatory variables on specific quantiles of the response variable, and therefore offers a robust method to estimate how different tiers of economic variables responds to changes in the dependent variable (Hao and Naiman, 2007, Koenker and Hallock, 2001), and also uses the entire sample in estimating effects of the distribution. It might be more suitable using energy prices to target policies that promote conservation but most times, there is no data and such analysis are clouded by sample bias (Heckman, 1979). In order to account for the nonlinearity in the relationship between carbon dioxide emissions and some chosen economic variables, quantile analysis will present a more elaborate account of impacting factors, and reveal detailed information on the asymmetric and non-linear effects of all conditional variables on carbon dioxide emissions. It can capture the effect of abrupt changes in all economic variables on carbon emissions across different quantiles. The quantile regression (QR) model is stated as follows: 𝑄𝛼(𝐶𝑂2𝑡|𝐼𝑡) = ɸ𝛼[𝐸𝑆,𝐸𝐼,𝐶𝐼,𝐼𝑆,𝐿𝑃]𝑡 + Є𝑡,

𝛼 𝜖 (0, 1).

(2)

where 𝑄𝛼(𝐶𝑂2𝑡|𝐼𝑡) is the conditional quantile of carbon dioxide emissions, [𝐸𝑆,𝐸𝐼,𝐶𝐼,𝐼𝑆,𝐿𝑃]𝑡 are explanatory variables (energy structure, energy intensity, carbon intensity, industrial structure, labor productivity), ɸ𝛼 is the slope coefficient that measures the impact of explanatory variables on carbon emissions at quantile α, 𝐼𝑡 is the information set at time t, and Є𝑡 is the error term. The above model is less restrictive than the Ordinary Least Squares method, and the slope coefficient can vary across quantiles of our dependent variable (carbon dioxide

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emissions). Generally the parameters of quantile prediction model are estimated using a tick loss function as follows: 𝐿𝛼(𝑒𝑡 + 1) = [𝛼 ― 1(𝑒𝑡 + 1 < 0)]𝑒𝑡 + 1

(3)

where 𝑒𝑡 = 𝐻𝐶𝑂2𝑡Ǭ𝛼,𝑡 is the forecast error, Ǭ𝛼,𝑡 = 𝑄𝛼(𝐻𝐶𝑂2𝑡|𝜁𝑡) denotes the conditional quantile forecast computed at time t, α is a specific quantile of the distribution of carbon dioxide emissions, and 1(.) is an indicator function. One of the main advantages using quantile is that it understands the differential effect of variables on the entire distribution of carbon emissions. For example, considering a case when an independent variable has 𝛼0.1 > 0, this implies that the 10th percentile of carbon dioxide emissions is positively influenced by an increase in the independent variables. At the same time, 𝛼0.9 < 0 implies that the 90th percentile of carbon dioxide emissions is negatively influenced by an increase in the independent variables. Therefore considering only average effect of independent variable(s), might limit the impact and explanatory power of the dependent variable. Other advantages are that assumption about the error term is unnecessary and all coefficients are robust with respect to outliers. Statistical inference are based on the construction of confidence intervals for estimated parameters using the inversion of rank test described in Koenker (1994), Koenker and Hallock, (2001). The first order condition is obtained by minimizing the expected value of equation 3 with respect to the forecast function. 5. Empirical outcomes 5.1 Unit root test

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This research applied Levin-Lin (LL), Im-Pesaran-Shin (IPS), Fisher-ADF and PP test in conducting unit root tests and the outcomes are shown in Table 2. All test confirmed that variables were non stationary at levels, and became stationary after first difference. It is hereby inferred that variables are first differenced stationary. These empirical outcomes uncover the non stationary properties of carbon dioxide emissions, energy structure, energy intensity, industrial structure and labor productivity for China metallurgy industry and also established the foundation for further analysis. Insert table 2 5.2 Cointegration In order to check the existence of long run relationship in economic variables, Johansen cointegration test is applied due to dynamic relationships in economic variables. Trace and maximum eigenvalue statistics were used to determine the number of cointegration relationships as shown in table 3. The unrestricted co-integration rank test of trace confirm that there are four linearly independent cointegrating equations, while the unrestricted co-integrating rank test of maximum eigenvalue confirm one cointegrating equation. Both tests therefore reject the null hypothesis, confirming that there is cointegrating equations between carbon dioxide emissions and its driving forces (ES, EI, CI, IS) at 5 percent significant level. Insert table 3 5.3 Quantile estimates Quantile estimation showed how the effects of energy structure, energy intensity, carbon intensity, industrial structure and labor productivity varied across the levels of carbon

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dioxide emissions, results showed that these effects are not constant across the spectrum of carbon emissions as shown in table 4. Each quantile both describes the distribution of carbon dioxide emissions and the marginal effects of all explanatory variables visually on different quantiles of carbon emissions. Selecting nine quantiles to facilitate analysis (0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, and 0.90), estimation procedures are graphically captured in Figure 5. All explanatory variables were positive and statistically significant at all observed quantiles except at α(0.30), α(0.70) and α(0.80) where carbon intensity was not significant though positive. Among explanatory variables, carbon intensity had the greatest impact on carbon dioxide emissions across all quantiles where it was significant, showing that it is a major driving force that increase carbon emissions across most but not all metallurgy industry in China with the highest percentage recorded at α(0.10) = 1.444689, implying that a unit increase in carbon intensity will result to 144 percent increase in carbon dioxide emissions. This view is against most research crediting carbon intensity as the major impacting factor across all cities in China. From a quantile perspective, in some metallurgy industry in China, carbon intensity was not an issue but other factor(s). Some studies also confirmed a decreasing energy intensity in the industry which is obviously negatively related to the views on energy intensity from a quantile perspective. All other factors impacted carbon dioxide emissions across all quantiles and greatest impacting factor was energy intensity with a highest percentage at α(0.90) = 1.027335, then labor productivity with a highest percentage at α(0.40) = 1.0074, then industrial structure with a highest percentage at α(0.90) = 1.0023 and finally energy structure with a highest percentage at α(0.70) = 0.9837. Quantile estimates were able to account for outliers present in economic variables because median estimation was robust compared to mean estimates, this gave our model edge over previous studies where estimation methods encompass the mean. Different quantiles

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translates into different regions across China, and hence different metallurgy industries. A more detailed analysis revealing disparity in economic variables across China was unveiled, revealing that economic policies should vary across regions in China. Regions where carbon intensity have greater impact should deploy policies with carbon intensity as the core variable, other regions that uncovered normally overlooked economic variables as the main driving force for carbon dioxide emissions in the metallurgy industry of China like energy intensity, should deploy policies that will boost technology and new innovations in these areas, same should be applicable to energy structure, industrial structure and labor productivity. The uniqueness of the metallurgy industry must be considered when administering economic policies across the industry in China, with respect to key economic variables that are emissions friendly to a particular area. Figure 5 captures how our median model reacted to economic variables and as can be seen, all quantile lines tried as much as possible to explain the effects of outliers in economic variables. Insert table 4 and figure 5 here Averagely, the coefficient of ES, EI, CI, IS and LP at α(0.50) = 0.9722, 1.003, 1.1858, 0.9841 and 1.0015 respectively. This implies that a unit increase in any of these economic variables will influence carbon dioxide emissions by 97.2 percent, 100.3 percent, 118.6 percent, 98.4 percent and 100.2 percent approximately and the model goodness of fit was 99.4 percent, signifying that all explanatory variables had strong explanation for carbon dioxide emissions. Quantile model strongly affirms that China's metallurgy industry is highly energy intensive, and not the whole industry in China was impacted by carbon intensity. Graphical results for all quantiles conditioning on carbon dioxide emissions are further revealed in Figure 6, graph shows an incremental trend in carbon dioxide emissions as we progressed across quantiles that is

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relatively increasing, and was really steep after the 50th percentile. This signifies that more efforts are needed for the reduction of carbon dioxide emissions in the metallurgy industry. Insert Figure 6 here 5.4 Model fitting accuracy To check the forecast function validity of the model, we inserted the historical data of energy intensity, energy structure, carbon intensity, industrial structure and labor productivity from 1991 to 2070 into the quantile equation [3] and the fitted values of carbon dioxide emission in China's metallurgy industry were obtained. The fitting accuracy was high which indicates that this is a good model for forecasting. However, considering different quantiles in our analysis, we found out that the highest percentage increase is 53.46% and the lowest is 47.44% for any given year approximately averaging 51.43%. Carbon emissions in the metallurgy industry is abnormally high and requires drastic measures in order to reduce emissions. The uniqueness of the industry demands that holistic measures to reduce emissions must be discarded and policy targeting emissions for the industry should be considered regionally with their intrinsic characteristics. Energy structure which is the ratio of total amount of fossil energy consumption and total energy consumption was significant across the whole industry, measures to limit fossil energy should be encouraged and none carbon emitting energy sources developed, this will ensure the expansion of energy consumption to accommodate renewable energy. Energy intensity which translates into technology and innovations falls below expectation across the industry. There is a dire need for technological improvement and new innovations to make the industry high tech, where emissions will be minimal. Most but not all the industry was plagued by carbon intensity, while industrial scale and labor productivity were emissions friendly

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variables across the whole industry at different capacity and should be revamped regionally (quantilewise). Insert figure 7 here 6. Conclusion and policy suggestion Metallurgy has been crucial to the development of China and its economy as the world benefits from China's status as a global manufacturing hub, however, this development is accompanied by massive energy consumption and carbon dioxide emissions that has generally plagued the industry across China. The awareness on pollution and environmental degradation is now a keen interest among Chinese citizens, due to economic empowerment and improved standard of living, and in order to avoid any societal upheaval as result of pollution, government are now proactive and highly committed to curb carbon dioxide emissions by the introduction of different measures and economic policies targeting emissions reduction across high energy intensive industries and manufacturing sector of the economy. Because the last 24 years has witnessed a leap in carbon emissions from the metallurgy industry of China, resulting to an 8 percent annual increment of carbon dioxide emissions, it is imperative to uncover major culprit and emission enhancing agent in order to administer a feasible mitigation policy for the industry as its proportion of emissions in the total emissions of China carbon dioxide increased from approximately 10 percent to 17 percent nationally. To ensure that policy targeting industrial emissions are achieved, standard mean based and other traditional estimation models are practically not suitable because of their inefficiency to outliers, so adopting a quantile analysis, varying effects of energy structure, energy intensity, carbon intensity, industrial structure and labor productivity across the levels of carbon dioxide emissions were analyzed. Results showed

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that these effect were not constant across the spectrum of carbon dioxide emissions, where each quantile both describes the distribution of carbon dioxide emissions and the marginal effects of EI, ES, CI, IS and LP visually on different quantiles of carbon emissions. Estimates were able to account for outliers present in economic variables, whereby quantiles translates into different regions across China, and different metallurgy industries. A more detailed analysis revealing disparity in economic variables across China was unveiled, revealing that economic policies should vary across regions in China. All explanatory variables were positive and statistically significant at all observed quantiles except carbon intensity that was not significant but positive at α(0.30), α(0.70) and α(0.80). Among explanatory variables, carbon intensity had the greatest impact on carbon dioxide emissions across all quantiles where it was significant, then energy intensity, labor productivity, industrial structure, and energy structure. Carbon intensity was not the most dominating variable as other research concluded but rather energy intensity, which is the energy consumption per unit of output. Regions where carbon intensity have greater impact should deploy policies with carbon intensity as the core variable, other regions that uncovered normally overlooked economic variables as the main driving force for carbon dioxide emissions in the metallurgy industry of China like energy intensity, should deploy policies that will address this economic variable, same should be applicable to energy structure, industrial structure and labor productivity. The uniqueness of the metallurgy industry must be considered when administering economic policies across the industry in China, with respect to key economic variables that are emissions friendly to a particular area and holistic measures to reduce carbon emissions must be thoroughly addressed and policy targeting emissions for the industry should be considered regionally with their intrinsic characteristics. Energy structure which is the ratio of total amount of fossil energy consumption and total energy consumption was significant across the whole

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industry, measures to limit fossil energy should be instituted and none carbon emitting energy sources deployed across the industry, this will ensure the expansion of energy consumption to accommodate renewable energy. Energy intensity which translates into technology and innovations falls below expectation across the industry. Generally, changes in energy intensity reflect progress in technology, and because it was the most emissions friendly economic variable across all regions (quantiles), there is an urgent need for technology advancement in the industry, most especially across metallurgy industry in lack of modern technology. This will significantly limit emissions and unveil a new high tech industry with minimal carbon emissions. Quantiles also revealed that most but not all the industry was plagued by carbon intensity, while industrial scale, which is the number of employees that have direct impact on energy consumption in the industry should be minimized optimally alongside their regional impact capacity, and labor productivity, which is the added value per unit of labor should be improved too with reference to regions (quantiles) significant. In lieu to this findings, the following suggestions will be valuable for the industry:  Upgrading the industry is very important in order to witness positive returns on any economic policy targeting carbon dioxide emissions mitigation. The industrial structure should be optimized by prioritizing the uniqueness of its energy intensity by specifically developing high tech metallurgy industry and faze out their low end counterpart gradually by setting up a time frame that must be adhered to, in order to achieve this goal.  The managerial levels and operational efficiency of the industry must be improved because this will transfer into labor productivity that will ensure the development of the industry while reducing its carbon dioxide emissions simultaneously. We believe

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that with the enhancement of environmental regulations and the transformation and upgrading of all regional metallurgy industries, the industry will enter a new normal period of development and during this period, labor-intensive employment benefits that are due to high-polluting industries will gradually be offset by low-polluting, capital-intensive industries.

REFRENCES [1] Boqiang Lin., Nelson I. Benjamin. Determinants of industrial carbon dioxide emissions growth in Shanghai: a quantile analysis. Journal of Cleaner Production 2019, 217:776-786. [2] www.economywatch.com/world_economy/china/industry-sectors. [3] Boqiang Lin., Du Z. Promoting energy conservation in China's metallurgy industry. Energy Policy, 2017, 104:285–294. [4] Zhao, Y., Liu, Y., Zhang, Z., Wang, S., Li, H., Ahmad, A., 2017. CO2 emissions per value added in exports of China: A comparison with USA based on generalized logarithmic mean divisia index decomposition. Journal of Cleaner Production DOI: jclepro.2017.01.031 [5] Sumabat, A.K., Lopez, N.S., Yu, K.D., Hao, H., Li, R., Geng, Y., Chiu, A.S., 2016. Decomposition analysis of Philippine CO2 emissions from fuel combustion and electricity generation. Applied Energy 164, 795–804.

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[6] Boqiang Lin., Xie, X., 2016. CO2 emissions of China's food industry: an input–output approach. Journal of Cleaner Production 112, 1410–1421. [7] Kucukvar, M., Egilmez, G., Tatari, O., 2014. Sustainability assessment of US final consumption and investments: triple-bottom-line input–output analysis. Journal of Cleaner Production 81, 234–243. [8] Chen, G., Hadjikakou, M., Wiedmann, T., 2016. Urban carbon transformations: unraveling spatial and inter-sectoral linkages for key city industries based on multi-region input–output analysis. Journal of Cleaner Production DOI:jclepro.2016.04.046. [9] Zhang, X., Zhao, X., Jiang, Z., Shao, S., 2017. How to achieve the 2030 CO2 emissionreduction targets for China's industrial sector: Retrospective decomposition and prospective trajectories. Global Environmental Change44, 83–97. [10] Sun, W., He, Y., Chang, H., 2017. Regional characteristics of CO2 emissions from China's power generation: affinity propagation and refined Laspeyres decomposition. International Journal of Global Warming11(1), 38–66. [11] Rahman, M.S., Shahari, F., Rahman, M., Noman, A.H.M. The interdependent relationship between sectoral productivity and disaggregated energy consumption in Malaysia: Markov Switching approach. Renewable and Sustainable Energy Reviews 2017, 67, 752–759. [12] Hasanbeigi, A., Harrell, G., Schreck, B., Monga, P., 2016. Moving beyond equipment and to systems optimization: techno-economic analysis of energy efficiency potentials in industrial steam systems in China. Journal of Cleaner Production 120, 53–63.

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[13] Chen, L., Xu, L., Xu, Q., Yang, Z., 2016. Optimization of urban industrial structure under the low-carbon goal and the water constraints: a case in Dalian, China. Journal of Cleaner Production 114, 323–333. [14] Guerron-Quintana, P., Inoue, A., Kilian, L., 2017. Impulse response matching estimators for DSGE models. Journal of Econometrics 196(1), 144–155. [15] Shawkat H., Duc, K.N., Ricardo M.S., 2014. Energy prices and co2 emission allowance prices: A quantile regression approach. Energy Policy 70, 201-206. [16] Nikhil Kaza, 2010. Understanding the spectrum of residential energy consumption: A quantile regression approach. Energy Policy 38, 6574-6585. [17 Boqiang Lin., Nelson I. Benjamin. (2019). Impact of energy technology patents in China: A panel quantile outlook. (submitted for publication). [18] Ke, Li., Boqiang Lin. (2015). Impact of energy technology patents in China: Evidence from a panel cointegration and error correction model. Energy Policy 89, 214-223. [19] Boqiang Lin., Nelson I. Benjamin. Influencing factors on carbon emissions in China transport industry. A new evidence from quantile regression. Journal of Cleaner Production 2017;150:175-187. [20] Boqiang Lin., Xie CP. Estimation on oil demand and oil saving potential of China's road transport sector. Energy Policy 2013;61:472-82. [21] Hao, L., Naiman, D., 2007. Quantile Regression. Sage, London UK.

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[22] Koenker, R., Hallock, K.F, 2001. Quantile regression. Journal of Economic Perspectives 15(4), 143-156 [23] Heckman, J.,1979. Sample selection bias as a specification error. Econometrica 47 (1), 153161. [24] Koenker, R.W., 1994. confidence intervals for regresiion quantiles, In:Madi, P., Huskova, M. (Eds), Asymptotic Statistics. Springer-Verlag, New York, pp349-359.

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40000 35000 30000 25000 20000 15000 10000 5000

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Figure 1. Global carbon dioxide emissions (million tons).

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Iron & Steel Industry

Nonferrous Metals Industry

Figure 2. Total output value of China's metallurgy industry (billions Yuan).

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Figure 3. Total energy consumption of China's metallurgy industry (million TCE).

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Figure 4. Carbon dioxide emissions from the metallurgy industry of China (million tons)

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LOGES

12.5

-.25

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Quantiles of Normal

Quantiles of Normal

LOGCO2

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Quantiles of LOGES

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LOGCI

5.2

-.35

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Figure 5. Quantiles of normal plot.

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Figure 6. Quantile plot of carbon dioxide emissions in the metallurgy industry of China (million

tons).

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Figure 7. The plots of actual and fitted values for carbon dioxide emissions in metallurgy industry of China (million tons).

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Table 1. Summary of economic variables Abbreviation

Multiplication

Economic meaning

factor ES

𝐸𝐹 𝐸

Energy structure

EI

𝐸𝑌

Energy intensity

CI

𝐶𝐸 𝐹

Carbon intensity

IS

W

Industrial scale

LP

𝑌𝑊

Labor productivity

Table 2. Panel unit root tests Series

LL test

IPS test

Fisher-ADF test

Fisher-PP test

ln (𝐶𝑂2,𝐸𝑆,𝐸𝐼,𝐶𝐼,𝐼𝑆,𝐿𝑃)

0.0679

2.3881

2.6651

3.0551

∆ln (𝐶𝑂2,𝐸𝑆,𝐸𝐼,𝐶𝐼,𝐼𝑆,𝐿𝑃)

-5.9318*

-6.1517*

57.0747*

59.1876*

where * denotes statistical significance at 1% level.

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Table 3. Results of Johansen cointegration test. Hypothes ized No. of CE(s )

Eigenvalue

Trace Statis tic

0.05 Critical Value

Prob.**

None * At m os t 1 * At m os t 2 * At m os t 3 * At m os t 4 At m os t 5

0.898464 0.732521 0.662140 0.554672 0.396415 0.150781

135.7056 85.38396 56.37223 32.49950 14.70275 3.595650

95.75366 69.81889 47.85613 29.79707 15.49471 3.841466

0.0000 0.0017 0.0065 0.0238 0.0656 0.0579

Trace tes t indicates 4 cointegrating eqn(s ) at the 0.05 level

Hypothesized No. of CE(s) None * At most 1 At most 2 At most 3 At most 4 At most 5

Eigenvalue

Max-Eigen Statistic

0.05 Critical Value

Prob.**

0.898464 0.732521 0.662140 0.554672 0.396415 0.150781

50.32162 29.01173 23.87273 17.79675 11.10710 3.595650

40.07757 33.87687 27.58434 21.13162 14.26460 3.841466

0.0025 0.1706 0.1392 0.1376 0.1489 0.0579

Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level

Table 4. Quantile estimates Quantiles 𝑙𝑛𝐸𝑆 𝑙𝑛𝐸𝐼 𝑙𝑛𝐶𝐼 𝑙𝑛𝐼𝑆 𝑙𝑛𝐿𝑃 Pseudo R2

α(0.10) .9504592 (0.000)* .9795575 (0.000)* 1.444689 (0.033)** .9869553 (0.000)* .9852308 (0.000)* 0.9920

α(0.20) .8974779 (0.000)* .9859891 (0.000)* 1.154836 (0.043)** .9788539 (0.000)* .9880565 (0.000)* 0.9912

α(0.30) .9494419 (0.000)* 1.010648 (0.000)* .7877543 (0.390) .9892092 (0.000)* 1.007354 (0.000)* 0.9921

α(0.40) .9771183 (0.000)* 1.014183 (0.000)* 1.030478 (0.043)** .9878927 (0.000)* 1.007409 (0.000)* 0.9931

α(0.50) .9722141 (0.000)* 1.003138 (0.000)* 1.185846 (0.000)* .9812887 (0.000)* 1.000804 (0.000)* 0.9938

α(0.60) .9638777 (0.000)* 1.004745 (0.000)* 1.15008 (0.022)** .9841444 (0.001)* 1.001528 (0.000)* 0.9937

α(0.70) .9837075 (0.000)* 1.000448 (0.000)* .9185787 (0.060) .9930983 (0.000)* 1.001344 (0.000)* 0.9933

α(0.80) .9423551 (0.000)* 1.001453 (0.000)* 1.03845 (0.227) .9958684 (0.000)* .9965384 (0.000)* 0.9931

α(0.90) .9020733 (0.000)* 1.027335 (0.000)* 1.137791 (0.000)* 1.002324 (0.000)* .9993966 (0.000)* 0.9934

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The numbers in parenthesis are p-values and the asterisk (*, **) denotes statistical significance at 1% and 5% respectively.