Can technological learning significantly reduce industrial air pollutants intensity in China?—Based on a multi-factor environmental learning curve

Wang, Wenxi; Yu, Bo; Yao, Xilong; Niu, Tong; Zhang, Chentao

doi:10.1016/j.jclepro.2018.03.028

Cited by 27 publications

(14 citation statements)

References 64 publications

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“…The concentrated population aggravated the energy-intensive product demand [73,74], thereby promoting APEs. The result that the population structure had a small promoting effect on primary APEs was supported by Lyu et al (2016) and Wang et al (2018) [43,75]. Moreover, the urbanization process was always associated with housing and infrastructure construction, large demand for energy-consuming commodities, and heavy vehicle emissions, thereby promoting APEs.…”

Section: The Regional Energy Intensity Effect Curbed Apesmentioning

confidence: 98%

Decomposition Analysis of Factors that Drive the Changes of Major Air Pollutant Emissions in China at a Multi-Regional Level

Yang

Miao

et al. 2019

Sustainability

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The regional emission reduction determines the national emission reduction for one country, and the differences in regional economic characteristics may result in regional differences in air pollutant emissions (APEs). In this regard, this study constructs a regional contribution index of different factors through an extended LMDI (Logarithmic Mean Divisia Index) method and investigates regional differences in factors driving the changes of China's major APEs (SO 2 , NO x , and PM 2.5 ). It reveals that the regional emission efficiency effect was a key inhibitory factor on APEs, which was more obvious in the eastern and northern regions. The regional energy intensity had greater inhibitory effects on SO 2 and NO x than on PM 2.5 , and these inhibitory effects were more obvious in the eastern region. The regional population structure promoted APEs for northern, southern, northeastern, and eastern regions, and inhibited APEs for central, southwestern, and northwestern regions. The regional relative income had a slight effect, which curbed APEs for northern, eastern, southern, and northern regions. The national economic growth effect was the key factor in promoting APEs, which was obvious in eastern and northern regions, followed by southern, central, and southwestern regions. Policy implications are put forward based on empirical results.Sustainability 2019, 11, 7113 2 of 22 reduction target for the 13th Five-Year Plan (FYP) (2016-2020) that by 2020 the energy consumption per 10,000 RMB GDP would be cut by 15% in comparison to 2015, and SO 2 and NO x would be within 15.8 and 15.74 million tons, respectively, which are both 15% lower than the level of 2015. There are regional differences in APEs for China because of its serious imbalances in economic development. As for the air pollutant reduction, the national reduction target should be reasonably allocated to each province and municipality as a regional target, to smoothly realize the national emission reduction target. Therefore, it is urgent and important to study the regional differences in factors that affect APEs in China, which is conductive to offer references for designing policies according to regional differences and effectively reduce air pollutant emissions (APEs).Many previous studies touched on the issues related to air pollutant emission trends and characteristics [6][7][8][9], and the harm to the economy and human health [10][11][12][13][14][15][16][17][18][19]. Recently, more attention has been focused on analyses of factors influencing APEs. Some studies established econometric models to discuss the relationships between APEs and macroeconomic variables [20][21][22][23] and explore the effects of these macroeconomic variables on APEs [24][25][26][27]. Some scholars analyzed sources of APEs from the sectoral perspective and found that the power and transport sectors were the main sources [28][29][30], followed by the agriculture and household sectors [31,32]. The decomposition method has been widely used to decompose factors influencing energy u...

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Section: The Regional Energy Intensity Effect Curbed Apesmentioning

confidence: 98%

Decomposition Analysis of Factors that Drive the Changes of Major Air Pollutant Emissions in China at a Multi-Regional Level

Yang

Miao

et al. 2019

Sustainability

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“…Using independent variables such as per capita gross domestic product, energy intensity, and the proportion of tertiary industry in gross domestic product, and CI as a dependent variable, MELC is used to estimate the CI abatement potential for provinces and economic sectors in China. Since the MELC model is based on the Cobb-Douglas multiplicative exponential model, these independent variables used in the MELC model do not directly reflect the learning curve effects of EC [70]. In a combined MELC model with the traditional multi-factor learning curve model [52,71,72], Wang et al [73] have estimated the negative coefficient of learning by doing at −0.578, or the PR of 67% for China.…”

Section: Experience Curve Applications In Energy and Air Pollutionmentioning

confidence: 99%

Dynamic Trends of Fine Particulate Matter Exposure across 190 Countries: Analysis and Key Insights

2020

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Despite the fact that fine particulate matter (PM2.5) causes serious health issues, few studies have investigated the level and annual rate of PM2.5 change across a large number of countries. For a better understanding of the global trend of PM2.5, this study classified 190 countries into groups showing different trends of PM2.5 change during the 2000–2014 period by estimating the progress ratio (PR) from the experience curve (EC), with PM2.5 exposure (PME)–the population-weighted average annual concentration of PM2.5 to which a person is exposed—as the dependent variable and the cumulative energy consumption as the independent variable. The results showed a wide variation of PRs across countries: While the average PR for 190 countries was 96.5%, indicating only a moderate decreasing PME trend of 3.5% for each doubling of the cumulative energy consumption, a majority of 118 countries experienced a decreasing trend of PME with an average PR of 88.1%, and the remaining 72 countries displayed an increasing trend with an average PR of 110.4%. When two different types of EC, classical and kinked, were applied, the chances of possible improvement in the future PME could be suggested in the descending order as follows: (1) the 60 countries with an increasing classical slope; (2) the 12 countries with an increasing kinked slope; (3) the 75 countries with a decreasing classical slope; and (4) the 43 countries with a decreasing kinked slope. The reason is that both increasing classical and kinked slopes are more likely to be replaced by decreasing kinked slopes, while decreasing classical and kinked slopes are less likely to change in the future. Population size seems to play a role: A majority of 52%, or 38 out of the 72 countries with an increasing slope, had a population size of bigger than 10 million inhabitants. Many of these countries came from SSA, EAP, and LAC regions. By identifying different patterns of past trends based on the analysis of PME for individual countries, this study suggests a possible change of the future slope for different groups of countries.

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“…For example, Klaassen et al [18] estimate a two-factor learning curve model and conclude that cost reduction depends on both cumulative production and R&D investment. Wang et al [19] introduce a methodology to examine the effect of technological learning on industrial air pollutants intensity in China. They find that learning by doing can significantly reduce industrial air pollutants intensity through energy efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Such widely used quadratic cost functions, 2 ( ) and ℎ 2 ( ), imply increasing marginal costs of corresponding investment [26,27]. Meanwhile, following theoretical and empirical research ( [4,8,18,19], etc. ), investment costs decrease with the increasing of the accumulated experience (knowledge), so for simplicity we suppose that 1 (⋅) and 2 (⋅) decrease linearly with the accumulated experience (knowledge).…”

Section: The Model Of Division Of Product Innovation and Advertising mentioning

confidence: 99%

Dynamic Control of Product Innovation, Advertising Effort, and Strategic Transfer‐Pricing in a Marketing‐Operations Interface

Cheng

2019

Mathematical Problems in Engineering

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We develop a dynamic control model of a monopolist composed of two profit centers, e.g., an operations department in charge of the product innovation and a marketing department controlling advertising effort as well as the retail price. Meanwhile, knowledge accumulating in product innovation and advertising effort which lead to reducing the corresponding investment cost is considered. The customer inverse demand function depends jointly on the quality level as well as the product goodwill which can be improved by product innovation and advertising efforts. Our results show that the learning rates of product innovation and advertising effort affect the product innovation and advertising effort investments level. In addition, compared with the administered transfer-pricing, the negotiation between the two departments results in a lower transfer price as well as a higher retail price. In the meantime, the advertising effort is lower while the quality improvement effort is higher. What is more, higher profits to both departments and the firm can be brought about by the negotiation means.

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Can technological learning significantly reduce industrial air pollutants intensity in China?—Based on a multi-factor environmental learning curve

Cited by 27 publications

References 64 publications

Decomposition Analysis of Factors that Drive the Changes of Major Air Pollutant Emissions in China at a Multi-Regional Level

Decomposition Analysis of Factors that Drive the Changes of Major Air Pollutant Emissions in China at a Multi-Regional Level

Dynamic Trends of Fine Particulate Matter Exposure across 190 Countries: Analysis and Key Insights

Dynamic Control of Product Innovation, Advertising Effort, and Strategic Transfer‐Pricing in a Marketing‐Operations Interface

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