A multi-factor efficiency perspective to the relationships among world GDP, energy consumption and carbon dioxide emissions

Most of the research articles on climate change study the relationships between economic growth, and, carbon dioxide (CO₂) emission or energy consumption separately for analyzing the impacts of economic growth and energy consumption on global carbon dioxide emissions. In this paper, the linkages among CO₂ emissions, Gross Domestic Product (GDP) growth and energy consumption are studied simultaneously using Data Envelopment Analysis (DEA). The time period considered for the study is 1980-2001. The results show that world in the year 1980 was the most efficient in achieving the highest economic growth, emitting least carbon dioxide for a given level of energy consumption for that year. The efficiency index reduced in the next 8 years, fluctuated with a declining trend for the next 7 years, and began to rise from 1996 till 2001. The model is further extended in this paper for technology forecasting to identify the links between energy consumption and carbon dioxide emissions for achieving projected levels of GDP under two different assumptions on efficiency index. It has been identified using the forecasting model that, when the carbon dioxide emissions are restricted to the levels emitted in the year 1990 and when the efficiency index for the year 2025 is assumed to be at the level registered for the year 1980 (highest value), the non-fossil energy consumption needed to meet the GDP level projected for 2025 will be much smaller (35.46 quadrillion Btu for the reference GDP) than the values actually recorded in the year 1990 (44.59). However, the non-fossil energy consumption in 2025 (118.8) increases much more than the actual recorded in the year 1990 when the efficiency index in 2025 is assumed to be at the level registered for the year 1990.     

广州城市生活垃圾增长与用地需求预测

采用线性回归、对数增长、固定比率增长和HP滤波趋势分析等方法对广州2010～2025年的城市生活垃圾及现有处理模式下的用地需求进行了预测。基于对广州未来城市人口基本将保持在1800万规模的判断,选取对数增长预测方法进行预测。结果表明,到2015年、2020年和2025年,广州中心城区终端垃圾处理量分别约为306万吨、320万吨和331万吨,在现有的以填埋为主的处理模式下,垃圾填埋的土地需求分别为407亩、426亩和441亩。     

Combined forecast process: Combining scenario analysis with the technological substitution model

Forecasts can be improved by combining separate forecasts obtained by different methods. The complementary nature of the scenario analysis and technological substitution models means that combining the two can obtain improved forecasts. The former has the strength of dealing with the uncertain future, while the later offers data-based forecasts of quantifiable parameters. This study thus proposes a process for combining the scenario analysis with the technological substitution model for discussing new generation technological developments. The proposed process not only has the strengths of scenario analysis, but also contains features that scenario analysis lacks, including predicting annual developments for future years, considering old technology development, and forecasting substitution of old technologies. This study uses the forecast of the market share of Fiber to the x in Taiwan over the next ten years as an example illustrating the proposed combined forecast process.     

A note on the diffusion of innovation

The paper deals with the process of substitution between technologies in a framework of increasing returns to scale. The approach stresses the interaction between capacity expansion and market demand as explanations of the diffusion of technologies into their niches. The demand and supply sides of the diffusion process are therefore brought together to determine simultaneously patterns of output and prices.It analyzes the dynamics of the substitution path, where a logistic diffusion process for the new technology is assumed, and determines the substitution curve between the old technology and the new one.     

Assessing market penetration combining scenario analysis, Delphi, and the technological substitution model: The case of the OLED TV market

The technological substitution model, proposed in 1971, was designed to analyze the penetration process of new-generation technologies replacing old ones. It was expanded in 1979 to consider more than two generations and the substitution process among various entities in the market. However, this expanded model is often limited by a relative lack of data on the latest-generation technology.On the other hand, scenario analysis, as another breed to forecasting tool, provides rich and complex portraits of possible future development of technologies. However, it fails to provide quantifiable forecasts. Therefore, previous researchers combined these two methods to analyze the development of new technologies.Nevertheless, to make forecasting more holistic, the current opinions of seasoned experts should also be taken into account. However, because of the often-diverged expert opinions, a method for consensus building, such as the Delphi method, is also necessary. Therefore, we combined the scenario analysis with the Delphi method and the technological substitution model to analyze the development of a new technology, namely, the latest-generation television (TV), or the organic light-emitting diode (OLED) TV. We elaborate three possible scenarios, and forecast the market share of OLED TV and four other TV technologies in the global market over the next 10 years: cathode ray tube, rear projection, plasma display panel, and liquid crystal display.     

我国钢铁产业最优集中度的系统性测算——基于企业与产业双重效率目标诉求和2007年数据的实证

文章基于企业和产业双重效率目标诉求的系统视角,以及不同集中度产业中单个企业市场需求曲线变化规律的科学界定,就2007年我国钢铁产业的最优集中度进行实证测算。实证测算结果表明,当年我国钢铁产业的最优产量为33 000万吨,钢铁企业的适宜数量为5-10个,单个钢铁企业的适宜规模为3 300-6 600万吨。研究表明,国民经济各产业的最优集中度是可以通过模型构建和数量分析得到精确界定的,产业集中度的优化调整应以最优集中度的精确测算为基准。     

虚拟资源在我国粮食问题中的综合应用分析

本文在参照虚拟水概念基础上,引用了虚拟土、虚拟能概念,提出了虚拟资源概念。对2003年南北方主要以粮食为载体的虚拟水、虚拟土、虚拟能含量进行了量化分析与实证研究,得出北方粮食虚拟水含量高于南方0．18m3／kg,并且每万吨粮食需要多占用0．6亩土地,只在虚拟能消耗上略低于南方0．01吨标煤／吨。2003年在国内粮食理想平衡状态下,南方须从北方调入2964．5万吨粮食以填补需求缺口,相当于从北方携带了655．2亿立方米虚拟水、7411万公顷虚拟土和1041．8万吨标煤的虚拟资源。这在一定时期内平衡了地区粮食的供求,可这能否在全国范围内实现资源的最优配置还值得商榷。     

On a family of data-based transformed models useful in forecasting technological substitutions

This paper examines, via real data, some well known models for technology substitution analysis. We propose a family of data-based transformed models that will include the models under examination as special cases. The basic thrust of the paper is the recognition that for technology substitution analysis, the observations are time series data and hence are not independent. Also, the functional form of the model should be determined by both theoretical considerations as well as the data on hand. This suggests that the traditional ordinary least squares procedure used in estimating the parameters and the resulting forecasting procedures are not adequate. The existing models examined here are Fisher–Pry, Gompertz, Weibull, and Normal. We stress the statistical aspects of the models and their relative merits in terms of predictive power. The criteria used for the purpose of comparison are the mean squared deviation and the mean absolute deviation of the predicted values compared with the actual observations.     

The transactions demand for money in the presence of currency substitution: evidence from Vietnam

Currency substitution – the use of foreign money to finance transactions between domestic residents – is widespread in low income and transition economies. Traditionally, however, empirical models of the demand for money tend to concentrate on the portfolio motive for holding foreign currency, while maintaining the assumption that the income elasticity of demand for domestic money is invariant to the degree of currency substitution. A simple re-specification of the demand for money is offered which more accurately reflects the process of currency substitution by allowing for a variable income elasticity of demand for domestic money. This specification is estimated for Vietnam in the 1990s. Using a standard cointegration framework evidence is found for currency substitution only in the long-run but well-defined wealth effects operating in the short-run.     

Diffusion of Kyoto's clean development mechanism

To date, developed countries can only tap mitigation opportunities in developing countries by investing in projects under the Clean Development Mechanism (CDM). Yet CDM investments have so far failed to reach all of the high-potential sectors identified in IPCC reports. This raises doubts about whether the CDM will be able to generate an adequate supply of credits from the limited areas where it has proved successful. Our paper examines the current trajectory of potential mitigation entering the CDM pipeline and projects it forward under the assumption that the diffusion of the CDM will follow a path similar to other kinds of innovations. Projections are then compared to pre-CDM predictions of the mechanism's potential market size used to assess Kyoto's cost, in order to discern whether limits on the types of project entering the pipeline will also limit the eventual supply of certified emission reductions (CERs). The main finding of the paper is that the mechanism is on track to deliver an average annual flow of roughly 700 million CERs by the close of 2012 and nearly to 1100 million tons by 2020. Parameter tests suggest that currently identified CDM investments will exceed early model predictions of the potential market for CDM projects.     

Determining demand, supply, and pricing for emerging markets based on disruptive process technologies

A model for forecasting the likely market size and demand for an early-stage emerging process technology is considered. This method takes into account markets, supply, demand, supply/demand gap, pricing, implications to government policy, corporate strategy, and value of intellectual property. For the purpose of illustration, forecasting of microsystems is considered.     

A model for forecasting the substitution of one technology for another

Most technological changes can be described as a substitution of one material, process or product for another. Each such substitution, if successful, normally tends to follow an S-shaped (or “logistic”) curve: that is, it starts slowly as initial problems and resistances have to be overcome; then it proceeds more rapidly as the competition between the new and the old technology grows keener and the new technology gains an advantage; and finally, as the market for the new technology approaches saturation, the pace of substitution slows down. Sometimes, when the process is completed, the old technology continues to retain some specialized portion of the total market (i.e., a sub-market) for which it is particularly well adapted. In forecasting the course and speed of the substitution process especially when it has already begun and partially taken place, the simplest approach is to project a function having the appropriate S-shaped curve, using historical data to determine the free parameters of the function. While useful, especially where data are not available for a more sophisticated study, the simple curve-fitting techniques fail to take into account several important factors that affect economic and management decisions on the part of producers and intermediate users (as well as “final” consumers) and thereby influence the course which the substitution process is likely to take. To overcome this limitation, a simulation model has been developed at IR&T which allows some of these factors to be evaluated and incorporated explicitly and quantitatively. The model is described and its application is illustrated in the case of the substitution of plastic for glass in bottles. It is most applicable where the competing technologies are rather precisely defined, where a good deal of current technical and economic data are available, and where an in-depth analysis is desired. Because this particular forecast was made before the sudden precipitous increase in petroleum prices, which upsets the price relationships assumed in the forecast, there is discussion of the vulnerability of forecasts to political and other contra-economic developments.     

The effect of structural change on the self-reliance and interdependence of aggregate sectors: the case of Spain, 1980–1994

The objective of this paper is to analyse structural and technological change in the Spanish economy between 1980 and 1994 using the input–output tables of 1980, 1986, 1990 and 1994. First we obtain and compare four linkage components throughout the period for each of the nine aggregate sectors in which the industries are grouped. Second, using Structural Decomposition Analysis, we obtain a technological effect and a demand effect for each of the previous components. These pressures show that the process of the technological modernisation of the Spanish economy is strongly linked to the growth of the high- and medium-technology industries and the service industries, and the different contribution of these industries to the improvement of productivity. On the one hand, the technology industries increase productivity by using better technologies. On the other hand, the service industries do not raise it significantly and even reduce it by increasing their unit costs.     

Economic growth and the evolution of water consumption in Spain: A structural decomposition analysis

The aim of this paper is to examine how technology, processes of input substitution, and changes in final demand, all of which underlie economic growth, influence water consumption. This analysis is undertaken for Spain during a significant socio-economic period, from 1980, the beginning of the democratic era, to 2007, the onset of the current economic crisis.To this end, we construct water consumption series linked to a time series of input–output tables generated for the Spanish economy, and we develop a structural decomposition analysis to study mainly changes in water consumption embodied in final demand.We find that the growth in Spanish demand (all other things being constant) would have implied an increase in water consumption almost three times the growth actually observed. However, this demand effect is largely offset by technology and intensity effects, mainly due to changes in agricultural crops. Given the importance of the demand growth, the final demand effect is also analyzed in detail, broken down by categories as well as level and composition. Household demand and the increase of exports appear as key explicative factors.     

Determinants for forecasting technological substitution

This paper investigates the factors determining the rate of technological substitution and evaluates the prospects for forecasting models based on market-share data only. Studies on underlying causes for the substitution of one technology or product by another reveal the frequent presence of a number of factors. These factors are first discussed in general terms so as to establish their causal relationships with the rate of substitution. On the basis of the identified cause-effect relationships, a composite model is formulated that incorporates the impact of individual factors in a combination of additive and multiplicative interactions. The relative importance and sensitivity of different factors in describing the behavior of the substitution process is then studied through a system-dynamics application of the model. Finally, using three specific cases, a comparison is given between the forecasts made by the comprehensive model, incorporating factors that are known to have significant impact on the rate of substitution, with that of a generalized model for forecasting technological substitution.     

A Litterman BVAR approach for production forecasting of technology industries

Forecasting the production of technology industries is important to entrepreneurs and governments, but usually suffers from market fluctuation and explosion. This paper aims to propose a Litterman Bayesian vector autoregression (LBVAR) model for production prediction based on the interaction of industrial clusters. Related industries within industrial clusters are included into the LBVAR model to provide more accurate predictions. The LBVAR model possesses the superiority of Bayesian statistics in small sample forecasting and holds the dynamic property of the vector autoregression (VAR) model. Two technology industries in Taiwan, the photonics industry and semiconductor industry are used to examine the LBVAR model using a rolling forecasting procedure. As a result, the LBVAR model was found to be capable of providing outstanding predictions for these two technology industries in comparison to the autoregression (AR) model and VAR model.     

中国废水排放量趋势预测——基于任务相关性的机器学习方法

中国的水污染是一个非常严重的问题,为了控制水污染,对中国未来废水排放量进行准确预测具有十分重要的意义。从影响废水排放量的潜在变量中筛选出GDP、人均GDP和产业结构三个关键预测变量,采用1998—2017年中国省域数据和基于任务相关性的机器学习方法对各省份废水排放量趋势进行预测。实验结果表明,所建立的预测模型能够保持线性回归模型的可解释性,并获得较高的预测精度(RMSE、MAE和MAPE分别为0.0882、0.0643和0.5533)。预计全国年废水排放总量2025年最低将达到9081209万吨,最高9903864万吨;2030年最低将达到10864311万吨,最高13081922万吨。政府部门可根据预测结果制定废水减排计划和经济发展规划,在发展经济的同时维持生态环境的平衡。     

Applying the Grey prediction model to the global integrated circuit industry

This study examines the precision of the Grey forecasting model applied to samples based on demand and sales in the global integrated circuit (IC) industry. In doing so, the main objective is to explore which forecast model is most appropriate for the IC industry by comparing the empirical results from the Grey model (GM), time series and exponential smoothing. Furthermore, three residual modification models are applied to enhance the forecasting results. Empirical results indicate that the GM is better suited to short-term predictions than to mid- and long-term predictions. Meanwhile, the Markov-chain residual modification model achieves reliable and precise results.     

Forecasting demand for electricity in Maryland: An econometric approach

Most of the models for forecasting demand for energy are based on simple extrapolations of past trends or on a simple regression equation with price of the energy and the stock of appliances as explanatory variables. In this paper, an attempt has been made to derive static and dynamic multiple regression equations from economic theory of consumption and production (Section II). Historical data were fitted to these theoretical constructs to test the equations in terms of econometric theory and forecast the demand according to “higher order conditional interval forecasts”. The residential demand for electricity is a function of its price, price of its substitute, per capita income and a lagged demand variable for dynamic adjustment of actual demand to equilibrium demand for electricity. The forecasts of residential demand to 1990 are based on projections of exogenous variables such as residential price of electricity, per capita income and the estimated long run elasticity of demand (Section III). The nonresidential demand for electricity is a function of employment in that sector, sectoral prices of electricity and the lagged sectoral demand. The forecasts of nonresidential electricity demand are also based on projections of its independent variables (Section IV). The last section converts the total demand for electricity into the required generating capacities and juxtaposes them against the estimates of expected supplies available from the forecasts of the utilities. The paper concludes that the eighties will be faced with excess supply of electricity in Maryland, in case the assumptions of projections of independent variables hold good. The misallocation of resources inherent in such excess supplies could be avoided if realistic scenerios of future demand, as attempted in this paper, could be predicted.