Impact of different factors on the pollution-reduction and resource

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Impact of different factors on the pollution-reduction and resource-saving effects of cleaner production Peilei Zhang, Ning Duan, Zhigang Dan, Jin Luo, Feifei Shi, and Huifeng Wang ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.8b02052 • Publication Date (Web): 23 May 2018 Downloaded from http://pubs.acs.org on May 23, 2018

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ACS Sustainable Chemistry & Engineering

Impact of different factors on the pollution-reduction and resource-saving effects of cleaner production Peilei Zhang†, Ning Duan*,‡, Zhigang Dan*,‡, Jin Luo†, Feifei Shi‡, Huifeng Wang‡

†School of Environment, Tsinghua University, No. 30, Shuangqing Road, Haidian district, Beijing, 100084, China

‡Technology Center for Heavy Metal Cleaner Production Engineerings, Chinese Research Academy of Environmental Sciences, No.8, Dayangfang, Anwai Beiyuan, Chaoyang district, Beijing, 100012, China *Corresponding Authors E-mail list:

[email protected] (Peilei Zhang); [email protected] (Ning Duan*, corresponding author); [email protected] (Zhigang Dan*, corresponding author); [email protected] (Jin Luo); [email protected] (Feifei Shi); [email protected] (Huifeng Wang).

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ABSTRACT:

Cleaner production (CP) is one of the most important tools to propel sustainable development. This research investigates the influences of pollutant and resource type, industry sector, time, and region on CP implementation; and understanding these influences are conducive to further CP implementation. More than 241 CP cases in China were selected as the data source, and the reduction rate (rr) of pollutant generation or resource consumption was used to quantify the pollution-reduction and resource-saving effects of CP. Time and region cannot affect CP spontaneously. However, the positive attitude of the regional government is instrumental to CP promotion. Statistical inferences indicate that rr is significantly influenced by the pollutant and resource type, and industry. Due to the adoption of suitable technologies, the rr of heavy metals is higher than that of resources and pollutants. The rr of resources was found to be lower than that of pollutants. However, due to the cost reduction caused by resource saving, relevant cleaner technologies were more easily adopted by enterprises. The significant impact of industry was caused by the different resources and pollutants of different industries. CP should be implemented based on the specific resource and environmental issues that the industry confronts.

KEYWORDS: Cleaner production, Influence factor, Pollution reduction, Resource saving, Reduction rate

INTRODUCTION


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In the last 20 years, proactive environmental protection strategy has received unprecedented attention. It refers to reducing or eliminating pollutant generation by strengthening management, using clean materials, and adopting advanced technologies and equipment. In different regions and industries, the term proactive strategy has different terminologies, such as pollution prevention,1 cleaner production (CP),2 lean production,3 green chemistry,4 and so on. CP is one of the most widely used concepts, and compared to the other terminologies, it emphasizes more on the pollution reduction aspect throughout a product’s life cycle.5 Recent years have witnessed its prominent performances in pollution reduction and resource saving across regions6-10 and industries,11-15 gradually becoming one of the most important tools for propelling sustainable development. Understanding the impacts of different factors on CP is conducive to its promotion, and many authors have undertaken research in this field. de Guimaraes, et al. analyzed the effects of strategic drives and project management maturity on CP’s success.16 Stone identified organizational factors that contributed to CP and affected the potential for ongoing improvements.17 Other relevant research also covered the framework of the promotion program,18 promoting system,2 policy tool,

19

driving

forces20 and promotion barriers.21-23 However, these studies mainly concentrated on the influences of managerial or organizational factors, while some common physical factors such as time, region, pollutant and resource type, and industry sector were seldom focused on. Pollution-reduction and resource-saving are the distinct



characteristics of CP. The impacts of the common physical factors on it were also researched rarely. Therefore, this research aimed to primarily investigate the impacts of time, region, pollutant and resource type, and industry sector on CP implementation, and pollution-reduction and resource-saving. Clarifying it needs an array of data, coupling CP performance with different pollutants and resources, industry sectors, time, and regions. All these data should be screened by consistent criteria. Although many studies have reported relevant data, they were obtained from different studies under different screening criteria,24 which are not suitable to answer the abovementioned question. An appropriate approach involves systematically reviewing the previous CP cases. Therefore, selecting the data source was instrumental to this research. China is one of the earliest countries to build national CP centers,25 and has made considerable achievements.26 The assessment methods,27-28 industry applications,29-30 promotion model2 and driving factors20 of China’s CP have been reported widely. Research based on China’s CP cases not only helps analyze the influence of physical factors on the pollution-reduction and resource-saving effects of CP, but also is significant to CP promotion in other developing countries. Consequently, China was selected as the research area for this study. By analyzing the data of 241 CP cases of China, this study explored the abovementioned questions, deepening the understanding of CP, and also provided data support and policy suggestions for future CP promotion. METHODOLOGY 4 ACS Paragon Plus Environment

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Data Source The data were obtained from the Demonstration Program of Industrial Cleaner Production (the Program, hereafter), implemented by China for encouraging CP implementation in different industry sectors. Three criteria, as mentioned below, were used to screen CP cases. The first criterion guaranteed that the CP case conforms to the concept of pollution reduction in source. “In source” stresses that the pollutants were reduced or eliminated before the pure end-of-pipe treatments and during the production procedures. If the end-of-pipe technology recycled the pollutants in situ, then this was also treated as a CP case. The second criterion guaranteed that the pollution-reduction and resource-saving performance of CP had a reasonable benchmark scenario, which was the typical production scenario without CP implementation. The benchmark scenarios of all CP cases were described by relevant documents of the Program and were scrutinized by both industry and CP experts. The cases lacking a clear and reasonable benchmark scenario were removed. The third criterion guaranteed data credibility. Every CP case had several documentations including application report, monitoring report, and evaluation suggestions. All the data about pollution reduction and resource saving had to be clear and consistent in all the documents. Finally, 241 cases were obtained. Metric of Pollution Reduction and Resource Saving



Equation 1 calculates the reduction rate (rr) of pollutant generation or resource consumption to quantify the CP case’s performance.31 rr=1-ccp/cbs Equation 1 where rr: Reduction rate of pollutant generation or resource consumption; cbs: Amount of pollutant generated (or resource consumed) per unit product in a benchmark scenario; ccp: Amount of pollutant generated (or resource consumed) per unit product when implementing CP cases. One CP case can reduce or save several pollutants and resources. The rr of every pollutant and resource was calculated. To standardize the rr of different forms of energy (electricity, steam, hot water, and raw coal), each individual rr was converted to coal equivalent by China’s national standard.32 Analysis Approaches Most previous studies reported relevant results only by numerical descriptions, which, such as the mean, do not represent the overall data situation. It is difficult to determine whether a factor indeed affects a variable according to only the numerical descriptions. Therefore, apart from common numerical descriptions, several statistical methods were employed to analyze the effects of different factors on rr. Figure 1 shows the specific analysis process.


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Numerical descriptions

Graphic tools Analysis of variance

Statistical inferences

Kruskal-Wallis test Least significant difference Other methods

One factor did or did not impact the rr of CP

Figure 1. Analytical flow of the effects of different factors on CP Numerical descriptions. A numerical description describes the pollution-reduction and resource-saving performance of CP using the mean, median, and other statistics of rr. For the sake of intuition, a box plot was mainly used to show the different numerical descriptions of rr, which can graphically depict groups of rr through their quartiles and also display the overall trends of groups of rr. Moreover, a map was used to depict the CP cases’ regional distribution. Statistical inferences. Statistical inference involves deducing the properties of an underlying probability distribution by data analysis; and those used in this research include: Analysis of Variance, Kruskal-Wallis Test, Least Significant Difference, and other methods. Analysis of Variance is a classic method for analyzing the effect of factors on variables and is applied to diverse disciplines including environmental science.33-34 It quantitatively divides the data variation into the effects of the target factor and that of other factors, and then uses the ratio of the two types of quantified effects to determine whether the target factor significantly affects the data. This analysis mechanism of Analysis of Variance is very consistent with the actual situation that the rr of CP was jointly influenced by other factors besides the analyzed ones. In this 7 ACS Paragon Plus Environment


research, it was used to test whether the four factors had significant influences on rr. Moreover, Analysis of Variance requires the data to be a normal distribution and the variances are equal statistically. The Kolmogorov-Smirnov Test and Levine’s Test were used to test for the normality and homoscedasticity respectively. Kruskal-Wallis Test,35 which also can test the influence of different factors on rr, was used to further verify the result of Analysis of Variance. When both Analysis of Variance and the Kruskal-Wallis Test indicated that a factor did affect the rr significantly, the Least Significant Difference was used to explore the sub-factor in which rr was significantly differentiated from other sub-factors. For example, when both Analysis of Variance and the Kruskal-Wallis Test showed that the industry sector significantly affected rr, the Least Significant Difference was used to identify the industry sector in which the rr was significantly higher or lower than that of other industry sectors. For statistical test methods, test decision can be made in two mathematically identical ways—comparing the test statistic with the critical value, and comparing the p-value with the significance level (α). Different statistical test methods have different statistics; however, their p-values’ meanings are the same as the probability that observes a departure from the null hypothesis. If p30

Figure 3. Regional distribution of funded CP cases. For clarity, only the province regions mentioned in text were placed in.

Effect of region on reduction rate. When analyzing the region effect on rr, some regions were excluded because of the small sample size of rr. Shandong, Henan, Jiangsu, Anhui, Hubei, Hebei, Hunan, Sichuan, and Ningxia were included. Figure 2b shows that the median of Ningxia was the smallest (37.21%), while that of Hunan was the greatest (58.62%). The mean of Sichuan was the smallest (45.40%), while that of Hubei was the greatest (57.63%). From small to large, the median followed the following order: Ningxia< Sichuan< Anhui< Jiangsu< Shandong< Henan< Hebei< Hubei< Hunan; and the mean followed the order: Sichuan< Ningxia< Shandong< Anhui< Jiangsu< Henan< Hunan < Hebei< Hubei. According to Tables S3 and S4, the rr values of the nine regions passed the normality test and homoscedasticity test, satisfying the theoretical assumptions of Analysis of Variance. As shown in Table 1, since the p-value (0.77) was greater than 12 ACS Paragon Plus Environment

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the significance level of 0.05, the null hypothesis that the rr was the same for different regions, cannot be rejected. The Kruskal-Wallis Test indicated the same statistical conclusion (p=0.79). Comprehensively considering the numerical results of Figure 2b and the statistical test results, the rr is considered the same in different regions. Although the industrial development levels, the bases of CP development, and the degrees of participation in the Program were different across the regions, all CP cases were screened by the same criteria, so there was no difference among the regions. Effects of Pollutant and Resource Type on CP Sample sizes of reduction rate of different pollutants and resources. The sample size of rr can reflect to some extent what kind of pollutants or resources CP mainly reduces or saves. As shown in Figure 2c, from large to small, the sample size of rr followed the order: energy> wastewater> fresh water> other raw materials> chemical oxygen demand (COD)> heavy metals> other pollutants in wastewater> exhausted gas> solid waste> sulfur dioxide (SO2). In all resources, the rr samples of energy were the highest, while in all pollutants the rr samples of wastewater were the highest. Energy conservation and wastewater reduction are almost common concerns of all industrial sectors. Although many cleaner technology developments were not to save energy or reduce wastewater, but by benefitting from the advanced nature of cleaner technology, many CP cases can save energy or reduce wastewater more or less, leading to the large sample sizes of their rr. Additionally, the Program clearly stipulated that the application documents of every CP case should expound on the performances of CP projects in energy saving and other environmental benefits. The 13 ACS Paragon Plus Environment


direct emphasis on energy saving was also the reason for the large sample size of the rr of energy. Another noteworthy phenomenon was that although the literal meaning of “cleaner” was more biased toward pollution reduction, the rr sample size of pollution reduction was less than that of resource saving. A high initial capital cost is the main obstacle to CP adoption.21 Resource saving has multiple benefits, it not only reduces the amount of pollutant generation (like water conservation can reduce wastewater production), but also saves production costs. Therefore, relevant cleaner technologies are more favored by enterprises.38-39 Table 1. Statistical inferences on the influence of different factors on rr Factor

Methods

p

Analysis of Variance

0.06

Kruskal-Wallis Test

0.07


0.77

Kruskal-Wallis Test

0.79


Impact of different factors on the pollution-reduction and resource

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