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| Srl | Item |
| 1 |
ID:
106231
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2011.
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| Summary/Abstract |
This study examines changes in Chinese urban income distribution from 1987 to 1996 and 1996 to 2004 using nationwide household data and investigates the causes of these changes. The Firpo, Fortin, and Lemieux (2007, 2009) method based on unconditional quantile regressions is used to decompose changes in income distribution and income inequality measures, such as variance and a 10:90 ratio. The decomposition results show that wage structure effects, such as a widening gender earnings gap, increases in returns to college education, and increases in earnings differentials between industries, company ownership types, and regions, have been the major contributors to the overall increases in income inequality. It was also found that at different points on the income distribution (e.g., the lower or upper half), the contributing factors that increase income inequality are different.
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| 2 |
ID:
092163
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2009.
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| Summary/Abstract |
This article examines changes in China's welfare programs in the context of economic transition from planned economy to a market-oriented economy. Using the 1988 and 1995 Chinese Household Income Project (CHIP), we develop analytical models to study the critical impacts of institutional variables such as economic ownership types and economic sectors on key welfare programs in both rural and urban areas in China. Our findings show that the Chinese government drastically reduced welfare coverage for its citizens during the economic transitional period even though Chinese urban residents' welfare income was primarily determined by the type of their employment. Outperforming many other factors, work unit's ownership nature played a significant role in the provision of welfare benefits while the economic sectors largely failed to have any significant impacts. These findings indicate that China has been moving away from the active state model in welfare provision. Yet, an industrialization and resource-based welfare system has not been realized in China in the reform era.
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| 3 |
ID:
134877
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| Summary/Abstract |
We introduce new provincial level panel data on human capital in China from 1985 to 2010. Our estimation of human capital is based on the Jorgenson–Fraumeni lifetime income approach modified to fit the Chinese data, thereby allowing a more comprehensive measurement of human capital than traditional partial measurements, such as education. Our provincial data are adjusted for purchasing power parity via a living-cost index as well as for real values so that all values are comparable across the provinces and time. We discuss various characteristics of the data, including total human capital, per capita human capital, and labor force human capital, which in turn are disaggregated based on gender and urban or rural location. Our human capital estimates are compared with the provincial physical capital estimates and provincial GDP. As an illustration, we also use the data to estimate a production function and to decompose China's economic growth from physical capital, human capital, and TFP. Our results, compared with those that use traditional specifications, reassure us as to the reliability of our new China human capital data.
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| 4 |
ID:
177347
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| Summary/Abstract |
Agricultural fossil energy consumption increases carbon dioxide emissions and is a critical concern in China. Photovoltaic agriculture refers to combining agricultural activities and photovoltaic power generation without changing the agricultural land and affecting agricultural production. It is a new agricultural production approach and has been identified as an important measure to deal with environmental pollution and fossil energy consumption. The goal of this study was to analyze the key factors that influence the willingness, behavior, and willingness-behavior consistency of farmers to adopt photovoltaic agriculture. A survey with 643 participants was conducted in China. The bivariate probit model and the binary logistic regression were used to test nineteen influencing factors. The results showed that the proportion of farmers whose adoption willingness was consistent with the adoption behavior was 37.1%, whereas 62.9% of farmers exhibited inconsistency between adoption willingness and adoption behavior. Differences were observed in the key factors influencing the willingness, behavior, and willingness-behavior consistency of adopting photovoltaic agriculture. The usefulness perception and technical training had significant positive impacts on the adoption willingness, adoption behavior, and willingness-behavior consistency of the farmers, whereas the photovoltaic investment cost had a negative impact. The results of this study provide an understanding of the factors influencing the promotion and dissemination of photovoltaic agriculture, a basis for optimizing related policies, and references to facilitate the implementation of photovoltaic agriculture in rural areas in other countries.
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| 5 |
ID:
177312
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| Summary/Abstract |
The deployment of renewable electricity and electric vehicles (EVs) provides a synergistic opportunity to accelerate the decarbonization of both China's power and transportation sectors. Here, we evaluate the potential impacts of EVs by utilizing the SWITCH-China model designed to meet emissions constraints within its power sector while integrating the electrified transportation sector. We focus on how various EV stocks, and charging strategies (unmanaged versus smart charging) impact the power sector, in terms of generation and hourly grid operation, the capacity mix, and achieving the Paris Agreement goals. Large-scale deployment of EVs increases the need for generation capacity, while the implementation of smart charging requires 6.8%–14% less additional storage capacity. We calculate that power system integration costs to incorporate EVs range from $228 - $352 per EV. We show that a smart charging strategy saves between $43 and $123 per vehicle more annually in 2050 than a case with the same EV stock where the charging is unmanaged. Our results suggest that a 140 GW annual growth of renewables from 2020 to 2050, coupled with an aggressive EVs deployment using smart charging can put China solidly on a path to meet its ambitious carbon cap targets.
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