本研究包括3個研究議題。第1個議題旨為估算不同部門別（包括農業、工業、服務業與運輸業）的能源燃燒產生CO2排放之組成因素的貢獻量，係藉由拉氏指數法和算術平均迪氏指數法之加法型態，拆解5種不同的因素（包括：碳密集度、部門結構、能源密集度、人口及經濟規模等），觀察其對於CO2排放變動之影響。本文採用台灣1992-2008年的各部門別的資料作為分析的基礎，研究結果顯示，以上部門的經濟規模對於CO2排放的貢獻呈現巨幅的正向效果；人口因素則呈現微幅的正向或負向的影響；而碳密集度對於CO2排放減量有正面的影響，並發現此乃是構成改善能源結構並導致CO2排放減量的最重要因素；能源密集度因素的影響，除服務業以外，其餘部門均呈現負向影響，此一結果顯示，大部分部門要進一步改善其能源效率頗為困難，是以未來致力於減排的努力，應著重於使用乾淨能源，尤其是以再生能源作為替代能源 (Liaskas et al., 2000)；此外，值得注意的是，部門結構因素對於大部分的產業，如農業、工業和運輸業的CO2排放減量有正向的影響，據此可推論，我國的部門結構已漸趨向於低耗能產業（如服務業）發展。另外，本文採用近似不相關迴歸模型，探討各項政策工具（如環境稅、進口關稅）與經濟變數（如貿易條件及時間趨勢等）透過以上5種不同的組成因素，對於CO2排放變動的影響效果作一分析，其實證結果可供決策者制定減排政策的參考。 第2個議題係為建立節能減排的有效政策工具，須先詳實掌握各項政策工具對節能減排與經濟成長的影響，乃深入回顧相關文獻之理論與實證方法，據以建立適合台灣的3E聯立模型，並進行實證分析，藉以推估多項政策工具（如環境稅、關稅等及能源價格等）與經濟變數（如貿易條件、所得等）對於節能減排與GDP的影響。實證分析結果顯示，台灣之能源消費、CO2排放、及GDP對於各項政策工具與經濟變數之彈性不僅各異其趣，而且有些彈性並非固定不變，可隨時間經過動態調整。 第3個議題係利用台灣1992-2008年之農業、工業、服務業與運輸業等部門別的panel data，仿照Battese and Coelli (1995)提出之隨機邊界(Stochastic Frontier Analysis, SFA)模型，建構隨機生產邊界函數 (stochastic production frontier function)與隨機能源需求函數 (stochastic energy demand frontier function)，利用最大概似法估算出各部門的GDP與能源需求之隨機邊界與技術效率 (technical efficiency, TE)，並據此實證結果提出政策建議。 / The thesis includes 3 issues of research. The first research aims at identifying the factors that have influenced change in the level of various sectors (agriculture, industry, service and transport) CO2 emissions from energy use. By means of both Laspeyres index method and the arithmetic mean weight scheme expressed separately in the additive form, the observed changes are analyzed into five different factors: CO2 intensity, structural change, sectoral energy intensity, sectoral employing population and output level. The application study refer to 4 sectors of Taiwan between 1992 and 2008. The obtained decomposition results indicate that the examined sectors the value calculated for the output level effect present the highest value appearing positive contribution of CO2, and the contribution from population is slightly increased or decreased, while CO2 intensity has beneficially influenced the reduction of CO2 emissions, as well as the improvement of fuel mix found to be the most important factor that lead to the reduction of emissions. In most of the examined sectors for the energy intensity factor present positive effect on CO2 emissions, the only exception is service sector showing negative impact on CO2 emissions, which can be stated as Liaskas et al. (2000) that as further improvements in energy efficiency in most sectors become more difficult, efforts to reduce CO2 emissions will be predominantly directed towards the use of clean energy forms and especially towards the deployment of renewable energies. It also should be noted that structural change has positively influenced the abatement of CO2 emissions for the most sectors such as agriculture, industry and transport. We conclude it shifts towards less energy-intensive service sector, due to have negative influenced the observed decrease in CO2 emissions for higher energy use sectors (industry and transport) and agriculture,. In this article, we also use a seemingly unrelated regression to further investigate the policy tools how to change in CO2 emissions level by the five different factors. The results indicate that policymakers may reduce emissions considerably through various policy instruments. The second issue focuses on initiating effective policy to save energy and reduce emission, one needs to reasonably capture the potential impacts of various policy instruments on energy consumption, CO2 emission and economic growth, the second research, after extensively reviewing the literature, builds a locally ideal empirical model that facilitates the estimation of various policy elasticities. The empirical results indicate that policy elasticities may not only differ from one to the others, but also change dynamically, implying the 3E impacts of some policy instruments might be weakening over time. The main goal of the third article is to provide a detailed analysis of productivity and efficiency measurement for panel data on four different sectors from Taiwan over the period 1992-2008. We use a stochastic frontier model set by Battese and Coelli (1995) to build a stochastic production frontier function and a stochastic energy demand frontier function, which are estimated by maximum likelihood to obtain a stochastic frontier of GDP and energy demand, as well as technical efficiency. On this empirical results, we suggest that policymaker may simultaneously make top-down policies (green tax reform, increasing environmental tax etc.) and bottom-up policies (fuel price in line with prices of gas in global markets) to increase energy efficiency in different sectors.

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Considering the backward and forward linkages, the agro-industrial sector contributes about 12 percent of South Africa‘s GDP, and employs approximately 8.5 million people. In the Eastern Cape Province, the sector contributes about 1.9 percent of the Provincial GDP, and over 3 million people derive their livelihoods from subsistence smallholder farming. Despite its importance, agricultural productivity has stagnated for several years across the Eastern Cape rural communities. There have been several attempts by the government to improve the agricultural productivity on smallholder farms since the end of apartheid, especially through the establishment of small-scale irrigation schemes, subsidization of farm inputs, and provision of credit facilities and enacting a number of land reform policies. In spite of the government support, most rural communities like Qamata and Tyefu are still faced with high levels of poverty affecting 76 percent and 91 percent of the population, respectively. This research evaluated the current smallholders‘ production efficiency, and the link between smallholder farmers‘ human dimensions (entrepreneurial spirit and positive psychological capital, goals and social capital, and other efficiency related variables) with production efficiency and household commercialisation index/level. The study used participatory approaches for site selection, sample selection and data collection. The analysis was based on both information from informal interviews and formal primary data collection. The Data Envelopment Analysis and Stochastic Production Frontier techniques were used to determine the relative efficiencies of individual farmers and to identify the major factors that influence the efficiency of production. Overall, 158 farmers were interviewed both at Qamata and Tyefu irrigation schemes. Descriptive statistics of this study indicated that most of the farmers were men with an average age of 61 years, and mean household size of 4 persons with the household head having at least obtained some primary school education. Farming is the major source of livelihood for smallholders with an average income of R4527.49 per crop season. Smallholders use improved seeds, fertilizers and tractor for ploughing with less use of pesticides and herbicides. Although smallholder irrigators generate more gross margins from maize and cabbage enterprises, generally both categories of farmers exhibited a low average household commercialization index for maize and cabbage at 0.41 and 0.22, respectively. Both Data Envelopment Analysis and Stochastic Production Frontier results indicate that farmers are about 98 percent technically efficient in maize and cabbage enterprises, respectively. However, farmers were allocatively inefficient as they were under-utilizing seed and pesticides while over-utilizing inorganic fertilizers. Factors that are positively associated with technical efficiency in maize production included household size, farming experience, off-farm income, use of agro-chemical; gross margins and commercialisation level of maize output. Determinants of technical efficiency in cabbage enterprise included farming experience, amount of land owned, use of agro-chemicals, group membership and gross margins accrued to cabbage sales. Farmers‘ human dimensions that could be more positively and significantly associated with production, efficiency and household commercialisation level included risk taking (hope), innovativeness (confidence) and optimism for entrepreneurial/positive psychological capital. Farmers‘ goals included self-esteem and independence, and only external social capital which were identified to be more positively and significantly associated with farmers‘ production efficiency and commercialization level. The transition from homestead subsistence to commercial oriented small-scale irrigation farming is inevitable since smallholder irrigators earn more incomes from maize and cabbage and are relatively food secure. However, the key policy options that must be considered to address inefficiencies and improved commercialization level to aid the transition include: agricultural policies geared toward attracting youth in farming, improved quality of extension services, speeding up the land reform process, and formation of cooperatives and participatory policy formulation that takes full cognizance of the farmers‘ human dimensions. Since farmers‘ human dimensions as defined in the literature and this study are not things that are amenable to direct policy intervention, they can only be modified indirectly through policy actions that affect their determinants. This means that a number of the demographic and socio-economic characteristics such as age, sex and education level of household head, farming experience, size of land owned, crop incomes, source of water for irrigation and location of the irrigation scheme that govern the way people perceive reality and respond to them must be the focus of concerted policy actions over the medium to long term.

No description available.