China relied on a cheap labor force at the beginning of its economic reform and opening-up period, which began in the late 1970s, and has been under pressure from the international community to reduce its carbon dioxide (CO2) emissions. The 2019 novel coronavirus has had a significant impact on global carbon emissions. In the past year, the global economy has faced unprecedented challenges, including environmental protection and energy supply.

In the 14th Five-Year Plan (2021–2025), improvements in industry and manufacturing quality in China are emphasized, along with autonomy, safety, stability, competitiveness, and efficiency, as well as modernization of the industrial chain (Table 1).1

The Korean manufacturing sector is gradually reducing its dependence on foreign trade. Last year, the Ministry of Industry, Trade, and Energy of Korea announced the "Blueprint for the Future of the Era of Manufacturing Revival." The President of Korea stated that the government's goal was to ensure that the manufacturing industry of Korea was among the top four in the world by 2030. The pursuit of quantity dictated by the current industrial model is to be replaced by greater focus on the value-added ratio; the goal is to increase the current value-added ratio from 25% to 30% by 2030.

With the increased prominence of the concept of green growth in recent years, attention has focused on the problem of global warming. While there may be a conflict between economic growth and environmental conservation (i.e., as the economy grows, environmental pollution inevitably increases), some view economic growth and environmental protection as being in a complementary relationship, such that once economic growth achieves a certain level, economic growth will help alleviate environmental problems. China's CO2 emissions currently stand at 9,899 million tons; this accounts for 30.7% of global emissions, which is the largest proportion of any country. Korea's CO2 emissions currently stand at 578 million tons (1.8% of global emissions; Fig. 1).

Fig. 1.

World's Top 10 CO2 Emitting Countries

Data source: IEA Statistics, CO2 Emissions from Fuel Combustion, 2020. (Unit: million tons).

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Environmental concerns are among the main factors driving the move toward sustainable development (Mendez-Picazo, Galindo-Martín, & Castaño-Martínez, 2021; Romero-Castro, López-Cabarcos, & Piñeiro-Chousa,2022). In the present study, China's standard industrial classification system (20 types of industry) was applied to Korea's manufacturing sector, and the two countries were then compared in terms of technical efficiency (TE), potential for CO2 emission reductions, and likely costs thereof. To this end, the input distance function of Shephard (1970) was used. As a representative example, Hailu and Veeman (2000) analyzed industrial competitiveness using an input distance function approach. More specifically, they analyzed the marginal abatement costs associated with biochemical oxygen demand and total suspended solids discharged from Canadian pulp and paper mills during 1959–1994. Using the input distance function, Jin and Lee (2015) analyzed TE, shadow prices, and substitutability during 1981–2009 for the Chinese thermal power industry. In a more recent study, Chen and Jin (2020) explored productivity changes in the thermal power sector at the provincial level. Their model was based on the Törnqvist index, and marginal abatement costs of CO2 emissions by the thermal power sector during 2009–2016 were calculated in various Chinese provinces. When performing productivity analyses, appropriate selection and handling of input, output, and price variables are key. Finally, Muhammad et al. (2020) analyzed quarterly data pertaining to the market share of the textiles and clothing industries, and the CO2 per capita, during 1990–2018. For their empirical investigation, they applied the innovative quantile-on-quantile regression approach and the Granger causality test.

In this study, input distance functions for 20 manufacturing industries in China and Korea, which account for a large proportion of total CO2 emissions, were estimated using an analysis methodology applied in previous studies. The maximum reduction in CO2 emissions that could be achieved by each industry was estimated assuming 100% TEChe (2017). calculated CO2 prices for China's iron and steel industry at the provincial level, based on a quadratic output directional distance function. Unlike that study, we also estimate the marginal cost reductions herein. China and Korea have both introduced and implemented national emissions trading systems. Estimates of the potential cost savings of inter-industry emissions trading, within and between countries, could have policy implications.

The rest of this paper is organized as follows. We first describe the analysis data and estimation results for manufacturing industries in China and Korea, followed by a summary and our conclusions.

Let us assume that an enterprise uses an input vector x∈R+Ncomposed of N production elements to produceythe final product quantity. In this case, 'desired outputs (q∈R+H) and 'undesired outputs (b∈R+M−H)' are generated in the process of creating an output vectory∈R+M. Therefore, for the production technology that produces M outputs from N inputs, the input distance function of Shephard (1970) is defined as follows. It measures the maximum range in which all inputs can be reduced at the same rate without reducing the outputs (Färe and Grosskopf, 1990; Hailu and Veeman, 2000).2

Here, the input vectorx∈R+3 is divided into three production elements; capital(k), labor(l), and energy(e), and the output vector y∈R+2 is composed of two outputs; final product outputs (q), which are 'desired outputs' and CO2 emissions(u), which are 'undesired outputs.' G(y)refers to the input requirement set that enables the production of yand measures the degree to whichxthat can produceycan be maximally reduced proportionally.

xAndyrefer to the input and output vectors, respectively, andG(y)is an input requirement set that enables the production ofy. The input distance function has the following characteristics.x∈B(y)AndI(y,x)≥1are the necessary and sufficient conditions to be mutually valid, are first-order homogeneous functions forx, and are monotonically non-decreasing and concave forx. These are quasi-concave foryand are non-decreasing for goods. On the contrary, these are non-decreasing for 'undesired outputs' because production elements necessary to reduce pollution should be additionally inputted to reducey, which is the 'undesired outputs' regarded as pollutants if the produced quantity of the 'desired output'qis to be maintained.

1/I(y,x) Can be calculated, and the technical efficiency (TE) of Farrell (1957) can be measured from the definition of the input distance function. In other words, if the production is assumed to have been implemented on the boundary line ofI(y,x), which is an area where the enterprise is the most efficient, that is, isoquant curve, it will be the most efficient situation, and the value of the input distance function value will be 1. Cases where the value is greater than 1, refer to situations where production elements are excessively inputted, exceeding the isoquant curve, which is the appropriate state. On the contrary, as the difference from 1 becomes greater, the production inefficiency increases.

Considering situations where enterprises' cost minimization is not achieved, a situation where the total shadow cost is minimized is assumed, as shown by Eq. (3) using the shadow price vector w3(in this case ws∈R+3).

Cost functions such as CS(y,ws)=minx{wsx:I(y,x)≥1} can be derived from the optimal condition of the cost minimization problem, of which a constraint condition is the value of the input distance function. Here, w∈R+3 denotes the price vector of the input that minimizes the cost.

Hailu and Veeman (2000) presented that the marginal abatement cost can be estimated using the input distance function. If Eq. (3) is partially differentiated y, Eq. (4) will be established.

Meanwhile, after obtaining pjsandpisrespectively from Eq. (5), if pisis the same as the market price (pi) of the final product, the relationship between the two output shadow prices can be expressed as follows.

That is, the yield that must be abandoned to reduce one unit of CO2 additionally is measured. To calculate the CO2 shadow price in Eq. (6)I(y,x), which is the input distance function, must be estimated. To this end, the form of translog, as shown in Eq. (7), is taken. Where, γii′=γi′i,γjj′=γj′j

According to Aigner and Chu (1968), the coefficients in Eq. (7) are estimated using the linear programming method. The objective function is set, and constraint conditions such as monotonicity and homogeneity are satisfied while minimization is pursued.

In the present study, TE, CO2 shadow prices, and the potential for CO2 emission reductions were compared between the manufacturing sectors of China and Korea. The most competitive industry types were also identified. Cross-sectional data for the period 2015–2019 were analyzed under the assumption of 100% production efficiency (e.g., in terms of fuel consumption). The maximum potential CO2 reductions for manufacturing industries in both countries were estimated using the input distance function.

Industry classification methods differ between China and Korea, and there are also a relative lack of relevant data from China (Table 2). Therefore, the manufacturing sectors of China and Korea were reclassified based on energy consumption levels, to yield 20 manufacturing industry types for both countries. All variables were standardized such that they had a value of 1 at the midpoint of 2017.3

The input distance function used in the analysis comprises both desired and undesired outputs; the latter pertains mainly to CO2 (b), which is obtained by inputting capital stock (k), labor (l) and energy (e). The source for all of the statistical data for China, except CO2 emissions and energy, was the China Statistical Yearbook. The unit for both the final industrial output (q) and capital input (k; calculated according to the number of fixed assets) was 100 million yuan. l and e were quantified in terms of the average number of employees (rounded to the nearest 10,000), and the unit for the total amount of energy was tons (rounded to the nearest 10,000). The amount of energy used was obtained from the China Energy Statistical Yearbook, while b was estimated based on the carbon emission factors for fuel in China (obtained from the Intergovernmental Panel on Climate Change)4. A summary of the statistical data is presented in Table 3.

For Korea, q, l, and k were estimated based on mining industry data, manufacturing industry data, and a statistical survey published by the National Statistics Office. The total kwas estimated based on tangible fixed assets, which are calculated for the manufacturing sector every year, while b was estimated from the amount of bituminous coal used and carbon emission factors provided in the Statistical Yearbook of Energy of the Korea Energy Economics Institute Table 4. summarizes the statistics for the variables of interest. Table 5 and Table 6 shows the results obtained by applying the input distance function in Eq. (7) to data from the 20 major industries in China.

As shown in Table 8, the manufacturing industries "leather”, "wood and wood products”, "publishing and printing”, "coke and oil refining”, "medical care”, "non-metallic minerals”, "other electrical machinery and electrical converters”, "other manufacturing machinery and equipment”, "other transportation equipment”, and "furniture" had TE scores of 1, i.e., the highest possible value, which was not the case for any of these industries in Korea. The “electronics” industry had the lowest TE score (0.79); production efficiency was also relatively low for "compounds and chemical products”. Moreover, whereas the TE of the "metal" industry in Korea was low, it achieved the maximum possible score (i.e., 1) in China.

The input distance function values for the manufacturing industries were estimated to be in the range of 1.00–5.07, with an average of 1.78. As can be seen in Table 8, the TE scores for Korean industries were in the range of 0.1971–1.000. A score of 1 was achieved by six industries, namely "beverages”, "coke and petroleum processing”, "nonmetal minerals”, "automobile manufacturing”, "other transportation equipment”, and "furniture." The industry with the lowest TE score (i.e., 0.1971) was the “metal” industry, followed by "rubber and plastics”, "groceries”, and "compounds and chemical products” (all scores < 0.5). The average TE score for all 20 manufacturing sectors in Korea was 0.7074. Assuming that production processes follow an isoquant curve, by maximizing production efficiency in the Korean manufacturing sector, production inputs could be reduced by up to ∼30% without any reduction in outputs.

The average TE score of the 20 major manufacturing industries in China was 0.94 (range: 0.7–1.0), which was approximately 23% higher compared with that in Korea. China was technically superior to Korea in all manufacturing industries, except the "beverages" and "automobiles" industries.5 The average TE score of all 20 manufacturing sectors in China was 0.94. Assuming an isoquant curve, as discussed above for Korea, the production elements could be reduced by up to ∼6% without any reduction in outputs.

CO2 emissions from the manufacturing industry in China could be reduced by an estimated 75.98 million tons, mostly in the "compounds and chemical products” industry (≥ 62.56 million tons; ∼82% of the total potential reduction). The "paper products”, "textile products”, and "food" industries also have potential for relatively large CO2 reductions.

The input distance function values for Korea's manufacturing industries, estimated using Eq. (7), are shown in Table 7. Farrell's TE scores can be obtained by calculating the reciprocals of the input distance function values.

Assuming 100% production efficiency, the product of the CO2 emissions calculation described above can be multiplied by (1−TE) to estimate the maximum possible reduction in CO2 emissions by the manufacturing sector of Korea. A lower TE is associated with greater potential for CO2 reduction. As shown in the second column of Table 7, CO2 emissions could be reduced by 88.11 million tons in total, with the “metal” industry accounting for 71.1 million tons. CO2 reductions exceeding 1 million tons could also be achieved by the “compounds and chemical products” and “food” industries (13.64 and 1.03 million tons, respectively).

The potential CO2 reductions were based not only on curbing excessive fuel input but rather on reducing all inputs by the same amount under the assumption of 100% TE. The three industries in the Korean manufacturing sector with the greatest potential for CO2 emission reductions together account for 96% of the total potential reduction.

Note: (a) The TEvalues estimated based on the input distance functions of China and Korea (shown in Fig. 2) were compared. The TE of the "coke and oil refining”, "medical care”, "other transportation equipment”, and "furniture" industries were all estimated to be 1. In addition, isoquant curve analysis demonstrated that production inputs were excessive compared with outputs in Korea. Shadow prices for the Chinese manufacturing industry were estimated; the “electronics” industry had the lowest price (US$0.08/ton), while the “non-metallic minerals” industry had the highest price (US$9.73/ton; identical to that for Korea).

Fig. 2.

Differences between China and Korea in TE values by sectora.

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The average shadow price for Chinese industries was estimated to be US$1.41/ton, while pbs, which is the weighted average of CO2 emissions for all industries, was US$6.57/ton (more than twice that in Korea). The pbs of all industries except the "non-metallic minerals”, "compounds and chemical products”, "coke and oil refining”, and "textile products" industries was estimated to be < US$1/ton.

As shown in Table 8, the shadow price of the “electronics” industry in China was the lowest among all industries, at US$0.08/ton, which was also lower than that in Korea. Thus, the technological competitiveness of this industry was higher in Korea than China. Industries with greater CO2 emissions due to the use of fossil fuels have lower marginal abatement costs.

By implementing the cap and trade system in the manufacturing sector, carbon allowances would be imposed on industries emitting relatively large amounts of CO2. To estimate the potential cost savings associated with emissions trading, the 10 largest CO2-emitting industries in Korea and China were analyzed (Table 9). Under our analysis scenario, individual industries are obligated to reduce their CO2 emissions by 10% Table 10. summarizes the simulation results for the two countries.

In China, pbs was estimated as US$6.57. The “non-metallic minerals” industry could lower its abatement cost by purchasing allowances rather than actually implementing abatement measures to meet the 10% cut in CO2 emissions mandated in our scenario, where the price of allowances (hereafter referred to as permits) is lower than the marginal abatement cost (by US$3.2 per ton). The total potential cost savings for this industry were estimated as US$127.79 million. Note that the “non-metallic minerals” industry pays the highest abatement cost because it has the highest CO2 intensity (c/q) among all manufacturing industries in China, as shown in Table 9. Nine other industries could benefit economically from the selling of permits. In particular, the “automobiles”, “medical care”, “beverages”, and “refined tea” industries, whose pbs and (c/q) values were the lowest among all industries, are the most likely to engage in trade with the “non-metallic minerals” industry; this is not only because they stand to gain the most economically by trading 1 ton CO2 emissions but also because such trade would facilitate CO2 emission reduction.

In Korea, the weighted average potential cost savings were estimated at US$10.44. The “chemical raw materials” and “non-metallic minerals” industries could reduce their abatement costs by US$85.05 and US$129.17 million, respectively, by purchasing permits. Potential permit sellers include the “rubber and plastics”, “metal”, “food”, “communication”, and “other electronic equipment” industries because of their relatively low pbs values.

The potential cost savings from CO2 emissions trading were compared between Korea and China. In the latter country, the market price of a 1-ton permit was calculated as US$6.57. The “non-metallic minerals” industry, whose CO2 shadow price was estimated as US$9.73, could reduce its abatement cost by US$3.2 per ton by purchasing permits. The potential cost savings of this action would amount to US$127.9 million. The effect of a 1-ton trade was found to be larger in Korea than China, given the higher estimated market price for a 1-ton permit in the former country. In any such trade, the “non-metallic minerals” industry would likely act as the buyer, with cost savings of US$85.05 and US$4.02 per ton. In both countries, nine industries in addition to “non-metallic minerals” could benefit economically by serving as sellers.

At the 17th UN Climate Change Conference of the Parties held in 2011, an agreement was made to extend the commitment period of the Kyoto Protocol by 5 years. Moreover, a legally binding treaty was entered into by China, the United States, and India, which are the largest greenhouse gas emitters. Meanwhile, at the Copenhagen Conference, Korea announced that it was targeting a 30% reduction in greenhouse gas emissions by 2020.6, 7

Since the key to sustainable low-carbon growth at the global level is cost-effective CO2 reductions, we estimated the potential CO2 reductions for 20 manufacturing industries in China and Korea using input distance functions and also derived marginal CO2 abatement costs.

Based on the assumption of 100% production efficiency, the maximum reduction in CO2 emissions for the Chinese manufacturing sector was estimated as 62.56 million tons, approximately 89% of which was accounted for by the "compounds and chemical products" and "paper products” industries. In the case of the Korean manufacturing sector, the industry with the highest potential for CO2 reductions was the metal industry (71.01 million tons). The average marginal abatement costs for Korean manufacturing industries was estimated as US$6.46/ton, and the weighted averagepbs was estimated as US$15.1/ton. In China, the “non-metallic minerals” industry showed the highest estimated shadow price, estimated as US$9.73/ton. The average marginal abatement cost for Chinese manufacturing industries was estimated as US$3.21/ton, and the weighted averagepbs was US$6.57/ton (more than twice that of Korea). The pbs of all industries, except the "non-metallic minerals”, "compounds and chemical products”, "coke and oil refining”, and "textile products" industries, was estimated to be < US$1/ton.

The “non-metallic minerals” industry in China could reduce its abatement cost by US$3.2 per ton by purchasing permits, for a potential cost savings of US$127.9 million. In Korea, the “chemical raw materials” and “non-metallic minerals” industries could reduce their abatement costs by US$85.05 and US$129.17 million, respectively, by purchasing permits.

Regarding the limitations of this study, the “iron and steel” and “cement” industries were excluded from the analyses due to a lack of data for China, where the technological competitiveness of these industries is inferior compared with Korea. Moreover, both quantitative and qualitative aspects of the analysis may have been affected by a lack of diversity in the Chinese data. In addition, given the heterogeneity among manufacturing industries, dummy variables for the individual industries would have been desirable, along with the use of transcendental functions to test for significant differences in production potential. This study used distance functions and was limited by a lack of probabilistic analyses. These weaknesses should be addressed in future studies comparing Chinese and Korean industries.