Characteristics of industrial hazardous waste generation in South Korea from 2008 to 2018 based on decoupling and decomposition analysis

The industrial hazardous waste (IHW) generation to meet consumption is steadily increasing, resulting in environmental, health, and social problems around the world. To address IHW at the source, it is critical to understand the generation characteristics and key drivers on industrial hazardous waste generation (IHWG). This study analysed the generation characteristics of IHW of South Korea from 2008 to 2018 by decoupling and index decomposition analysis using Log Mean Divisia Index (LMDI) model. South Korea presented unstable decoupling of IHWG from economic growth, so more effective waste management regulations are needed to support a stable decoupling. One most critical finding was that the factors of industrial output and industrial characteristic of IHWG-to-energy were major driving factors influencing the increase of IHWG, whereas those of industrial structure and energy efficiency affect to the decrease of IHWG in most industries. In addition, the result clearly confirmed that the contribution of driving factors affecting the IHWG differs by industry. These results provide significant policy insights that the South Korean government needs institutional improvement and refinement of customised IHW management according to the characteristics of IHWG.

Evaluation of China’s Targeted Poverty Alleviation Policies: A Decomposition Analysis Based on the Poverty Reduction Effects

To achieve comprehensive poverty alleviation and the establishment of a “moderately prosperous society” in China, it is crucial to evaluate the targeted poverty alleviation (TPA) policies. In this study, China’s poverty alleviation statistics and the Foster-Greene-Thorbecke (FGT) indices are used to measure the poverty reduction effects of the TPA policies. A panel regression model is applied to analyze the poverty reduction mechanism while the Shapley index decomposition method is used to analyze poverty reduction effects in terms of income growth and the income gap adjustment. The paper concludes that the poverty breadth index (H index), poverty depth index (PG index), and poverty intensity index (SPG index) from 2013 to 2019 show a significant decline overall. This indicates that the poverty reduction effect of the TPA policies is significant. In addition, the regression analysis shows that the implementation of TPA policies can significantly increase the income level of residents and narrow the income gap among residents in rural areas. Results of the Shapley index decomposition analysis revealed that the income growth effect and income gap adjustment effect accounted for 92.78% and 7.22% of the poverty reduction effects, respectively. So the focus of future poverty alleviation work is to combine the rural revitalization strategy and to continue increasing the income level and the income growth rate of poor groups, which will enhance the ability of impoverished residents to increase their income, further contributing to the alleviation of poverty.

Driving Factors for the Change of Fertilizer Use Intensity in China and Its Six Major Regions

China has invested excessive amounts of fertilizer on limited farmland, which poses a threat to human health and agro-ecological environment. While a plethora of studies have explored how to reduce the total amount of chemical fertilizer, and scant attention has been paid to how to reduce the chemical fertilizer use intensity (FUI). This paper aims to explore the driving factors for the change of FUI to answer this question. It uses the official statistics of China from 1997 to 2017, as well as index decomposition analysis (IDA) and Laspeyres index decomposition method to obtain the following results. The change of fertilizer use intensity can be affected by three factors: input-output ratio of fertilizer (IOR), unit labor output (ULO) and labor input per unit sown area (LIU). At the national level, IOR is the most important factor in reducing the use of chemical fertilizers, while ULO is the most important factor in increasing. The factor of LIU can not only reduce the intensity of fertilizer use, but also increase the intensity.  On a regional level, the fertilizer use intensity in Central-South China is the strongest, while that in the Southwest China is the smallest. Compared with other regions, the factors of ULO in Northwest China and IOR in East China have the greatest impact on fertilizer use intensity. In addition, LIU mainly reduces the intensity of fertilizer use in Northeast China, while this factor in North China is to increase the intensity. Our findings suggest that farmers should not increase labor productivity by investing chemical fertilizer. Improving the efficiency of fertilizer use and transferring rural labor force can reduce the fertilizer use intensity in China.

A Decomposition Analysis of the Korean Manufacturing Sector: Monetary vs. Physical Outputs

This study analyzes the changes in energy consumption of the Korean manufacturing sector using the index decomposition analysis (IDA) method. To capture the production effect based on actual physical activities, we applied the activity revaluation (AR) approach in the analysis. We also developed energy consumption data in terms of primary energy supply to consider conversion loss in the energy sector to avoid any distortions in the intensity effect. The analysis covers every manufacturing subsector in Korea over the period between 2006 and 2018. Combining two distinctive approaches from the previous literature, the AR approach and primary energy-based analysis gives us helpful findings for a climate policy. First, the overall activity effect estimated from the physical output indicator is lower than that from the monetary output indicator. The monetary indicator shows that the share of energy-intensive industries decreases, whereas the physical indicator shows the opposite. Second, in terms of energy efficiency, the intensity effect is estimated as an increasing factor of energy use, whereas inversed results are shown when we use the monetary indicator. Lastly, unlike the previous studies, the AR approach results indicate that Korean manufacturing sectors have been shifting toward an energy-intensive, so it is hard to anticipate positive intensity effects, which means decreasing energy consumption factor, for a while. These results support why analyzing the driving forces of energy consumption through the AR approach and primary energy base is highly recommended.

Decomposição do consumo de energia na indústria brasileira: efeitos do aumento da atividade entre 1970 - 2019

A indústria é um dos setores da economia que mais consomem energia, sendo responsável por 32% do consumo final em 2019. Compreender como se comporta o consumo da indústria ao longo das décadas, decompondo a variação do consumo entre os efeitos atividade, estrutura e intensidade, através da decomposição de números índices, é de grande importância e é o objetivo principal deste trabalho. Este trabalho inova ao utilizar o método “Index Decomposition Analysis (IDA)” para os dados de consumo de energia industrial. Conclui-se que o efeito atividade é o principal responsável pelo aumento no consumo de energia, já que captura a participação do aumento da produção industrial sobre o consumo de energia. Por outro lado, o efeito intensidade cresce no período, o que indica que a indústria brasileira está se tornando menos eficiente no consumo de energia. Já o efeito estrutura contribui reduzindo o consumo no período estudado, o que indica que os setores energo intensivos perderam participação na produção industrial. Além disso, a partir da estimação da equação de demanda de energia utilizando como proxy do consumo o efeito atividade, constata-se que a elasticidade renda da demanda de energia foi de 1,57% no período, já que a elasticidade preço da demanda não é significante.