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Статті в журналах з теми "Divisia index"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

Barnett, William, Kun He, and Jingtong He. "Consumption Loan Augmented Divisia Monetary Index and China Monetary Aggregation." Journal of Risk and Financial Management 15, no. 10 (October 2, 2022): 447. http://dx.doi.org/10.3390/jrfm15100447.

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Анотація:

Simple sum monetary aggregates are based on accounting conventions and have no aggregation theoretic foundations in economic theory. In contrast, Divisia monetary aggregates are directly derived from aggregation and index number theory. Credit card services cannot be included in simple sum monetary aggregates since accounting conventions cannot aggregate over assets and liabilities. However, microeconomic aggregation theory aggregates over service flows, not stocks, regardless of whether from assets or liabilities. As a result, it has recently been shown that Divisia monetary aggregates can be augmented to include credit card services and are available from the Center for Financial Stability in New York City. Other sources of consumer credit cannot be included in Divisia monetary aggregates for the United States since other sources of consumer credit in the United States are linked to specific groups of consumer goods and hence, violate the weak separability condition for the existence of an aggregator function. However, China produces a unique opportunity to broaden the Divisia monetary aggregates since sources of consumer credit, not limited to credit cards, are applicable to all consumption purchases and hence, do not violate the existence condition for an aggregator function. We report initial results with a broader Chinese Divisia monetary aggregate, including not only credit card services but also other broadly acceptable consumer loan services.

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2

ELGER, C. THOMAS, BARRY E. JONES, DAVID L. EDGERTON, and JANE M. BINNER. "A NOTE ON THE OPTIMAL LEVEL OF MONETARY AGGREGATION IN THE UNITED KINGDOM." Macroeconomic Dynamics 12, no. 1 (October 1, 2007): 117–31. http://dx.doi.org/10.1017/s136510050706035x.

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Weak separability is a key admissibility property in the Divisia approach to monetary aggregation. We test groups of U.K. household sector monetary assets for weak separability using new data underlying the Bank of England's benchmark revision of its household sector Divisia index. Nonparametric tests are used to identify four monetary asset groupings, which are weakly separable over all or almost all of the post-ERM period (1992:4–2005:1). We construct Divisia monetary aggregates for these four groupings and investigate their information content in two applications. The main findings are that Divisia money has direct effects on aggregate demand and that the growth rates of the nominal Divisia monetary aggregates Granger cause nominal output growth, but not inflation.

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3

Polat, Umurcan. "Divisia and Simple Sum Monetary Aggregates: Any Empirical Relevance for Turkey?" Journal of Central Banking Theory and Practice 7, no. 1 (January 1, 2018): 175–206. http://dx.doi.org/10.2478/jcbtp-2018-0008.

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AbstractIn consideration of channels through which monetary policy affects economic activity, the monetary aggregates have been mostly ignored by the monetary authorities instead of which shortrun interest rates have been given a priori role. These monetary aggregates are largely argued to fail in measuring the effectiveness of different monetary policy regimes in forecasting the macroeconomic fundamentals. Grounded on the “Barnett critique”, the formation of traditional simple-sum monetary aggregates assuming for perfect substitution among the components of the money supply is blamed for such a failure of money in explaining the real activity. Given increasing varieties of financial assets which have completely different “moneyness”, it is important to provide an alternative measure of the money supply. Hereby, the Divisia monetary aggregates which give different weights to different assets have arisen as an alternative approach. In this study, a Divisia index is constructed to test its predictive power on quantities and prices compared to its simple sum counterpart. Accordingly, a Divisia index is built-up for Turkish economy for the period 2006-2016 to see whether the utilization of the Divisia monetary aggregates in the conduct of monetary policy makes any difference compared to that of traditional simple sum money supply. Under different specifications, though the relative power of the Divisia aggregates in predicting quantity and price variables is found, still, it can be argued that theoretically well-rounded formation of the Divisia index is not that much empirically justified for the case of Turkey.

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4

Cysne, Rubens Penha. "Divisia Index, Inflation, and Welfare." Journal of Money, Credit, and Banking 35, no. 2 (2003): 221–38. http://dx.doi.org/10.1353/mcb.2003.0010.

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5

Vogt, Arthur. "The natural index and the divisia index on the straight line as well as the divisia index on the exponential line." Communications in Statistics - Theory and Methods 15, no. 12 (January 1986): 3567–82. http://dx.doi.org/10.1080/03610928608829330.

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6

Barnett, William A., and Biyan Tang. "Chinese Divisia Monetary Index and GDP Nowcasting." Open Economies Review 27, no. 5 (June 29, 2016): 825–49. http://dx.doi.org/10.1007/s11079-016-9406-z.

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7

Yzeren, J. "Fisher's ideal index numbers as natural Divisia results." Statistische Hefte 27, no. 1 (December 1986): 89–99. http://dx.doi.org/10.1007/bf02932560.

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8

Wang, Shuo. "Analysis on the Influence Factors of CO₂ Emission in France - Based on LMDI Approach." Advanced Materials Research 616-618 (December 2012): 1537–40. http://dx.doi.org/10.4028/www.scientific.net/amr.616-618.1537.

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Анотація:

This article uses Logarithmic Mean Divisia Index Method (LMDI) to analyze influence factors of emission in France during last 50 years, including energy use, GDP, carbon density, energy structure and population. Energy structure problem is proposed at the end of the article.

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9

Barnett, William A., and Van H. Nguyen. "Constructing Divisia Monetary Aggregates for Singapore." Journal of Risk and Financial Management 14, no. 8 (August 12, 2021): 370. http://dx.doi.org/10.3390/jrfm14080370.

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Анотація:

Since Barnett derived the user cost price of money, the economic theory of monetary services aggregation has been developed and extended into a field of its own with solid foundations in microeconomic theory. Divisia monetary aggregates have repeatedly been shown to be strictly preferable to their simple sum counterparts, which have no competent foundations in microeconomic aggregation or index number theory. However, most central banks in the world, including that of Singapore, the Monetary Authority of Singapore (MAS), still report their monetary aggregates as simple summations. Recent macroeconomic research about Singapore tends to focus on exchange rates as a monetary policy target but ignores the aggregate quantity of money. Is that because quantities of money are irrelevant to economic activity? To examine the role of monetary quantities as potential monetary instruments, indicators, or targets and their relevance to predicting real economic activity in Singapore, this paper applies the user cost of money formula and the recently developed credit-card-augmented Divisia monetary aggregates formula to construct monetary services indexes for Singapore. We produce those state-of-the-art monetary services indexes from Jan 1991 to Mar 2021. We see that Divisia measures behave differently from simple sum measures in the period before the year 2000, while interest rates were high. Credit-card-augmented Divisia monetary services move closely with the conventional Divisia monetary aggregates, since the volume of credit card transactions in Singapore is relatively small compared with other monetary service assets. In future work, we plan to use our data to explore central bank policy in Singapore and to propose improvements in that policy. By making our data available to the public, we encourage others to do the same.

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10

Lewbel, Arthur. "A Path-Independent Divisia-Like Index for PIGLOG Preferences." Economica 56, no. 221 (February 1989): 121. http://dx.doi.org/10.2307/2554500.

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11

Gogas, Periklis, Theophilos Papadimitriou, and Emmanouil Sofianos. "Money Neutrality, Monetary Aggregates and Machine Learning." Algorithms 12, no. 7 (July 5, 2019): 137. http://dx.doi.org/10.3390/a12070137.

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Анотація:

The issue of whether or not money affects real economic activity (money neutrality) has attracted significant empirical attention over the last five decades. If money is neutral even in the short-run, then monetary policy is ineffective and its role limited. If money matters, it will be able to forecast real economic activity. In this study, we test the traditional simple sum monetary aggregates that are commonly used by central banks all over the world and also the theoretically correct Divisia monetary aggregates proposed by the Barnett Critique (Chrystal and MacDonald, 1994; Belongia and Ireland, 2014), both in three levels of aggregation: M1, M2, and M3. We use them to directionally forecast the Eurocoin index: A monthly index that measures the growth rate of the euro area GDP. The data span from January 2001 to June 2018. The forecasting methodology we employ is support vector machines (SVM) from the area of machine learning. The empirical results show that: (a) The Divisia monetary aggregates outperform the simple sum ones and (b) both monetary aggregates can directionally forecast the Eurocoin index reaching the highest accuracy of 82.05% providing evidence against money neutrality even in the short term.

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12

Murni, Yetty, Eka sudarmaji, Sri Ambarwati, and Ismiriati Nasip. "Technical analysis on household energy consumption: LMDI decomposition index and innovative." IOP Conference Series: Earth and Environmental Science 1041, no. 1 (June 1, 2022): 012024. http://dx.doi.org/10.1088/1755-1315/1041/1/012024.

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Abstract For decades, Indonesia’s Economic Development had achieved great high growth rates by using more labor input, capital investment, physical and human, and fostering competition. Over time, Indonesia’s energy intensity, which historically always been high compared to other industrialized economies. This study aimed to analyze decomposition factors of energy consumption by analyzing the link between the other nine factors and household energy use. The Logarithmic Mean Divisia Index were used to identify the determinant variables that drove a household’s energy usage. This study used a Time-series VECM model for prediction model analysis. The Logarithmic Mean Divisia Index (LMDI) and decomposition index were used to identify the determining technological, economic, and social factors driving a household’s energy consumption evolution. We analyzed the outcomes after applying the LMDI. We provided econometric individual time series VEC models, which we used for three targeting models: social, economic, and technical factors. We discovered that economic variables were the most significant factors that aggravated the change in household energy consumption based on LMDI. Technical factors, on the other hand, were the lowest. We found that the government might reduce its energy use over time by improving technological factors. Energy efficiency had been a cornerstone in managing growing energy use. The declining relevance of fewer labor-force in the previous decade was reflected in the negative indication of social factors at home. We discovered that the models were more stable while using Time Series VECM.

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13

Belakhdar, N., M. Kharbach, and M. E. Afilal. "The renewable energy plan in Morocco, a Divisia index approach." Energy Strategy Reviews 4 (August 2014): 11–15. http://dx.doi.org/10.1016/j.esr.2014.06.001.

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14

Vaninsky, Alexander. "Factorial decomposition of CO2 emissions: A generalized Divisia index approach." Energy Economics 45 (September 2014): 389–400. http://dx.doi.org/10.1016/j.eneco.2014.07.008.

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15

Kang, Hyungsu, and Hyunmin Daniel Zoh. "Classifying Regional and Industrial Characteristics of GHG Emissions in South Korea." Energies 15, no. 20 (October 20, 2022): 7777. http://dx.doi.org/10.3390/en15207777.

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South Korea officially committed to reducing 40% of its total carbon emissions by 2030, but the country has a carbon-dependent economic structure based on the manufacturing industry. Additionally, the industrial structure of each region in South Korea is heterogeneous. In this regard, policymakers should analyze the carbon emission condition at a regional level because abatement aspects are heterogeneous by urban spatial production. However, although various studies have developed a methodology to evaluate the GHG emission condition, these studies failed to consider the fundamental aspect of regional heterogeneity. In this regard, this study suggests a quantitative method to assess the potential of the carbon neutrality of regions and industries by using both shift-share analysis and the Log Mean Divisia Index method. Shift share analysis is used to quantify the relation between the industry and regional characteristics, and the Log Mean Divisia Index method can decompose each effect for economic growth and technological progress. By combining these two methods, this study suggests four classifications to evaluate regional and industrial characteristics of GHG emissions and analyze each region’s emission status in terms of the mining and manufacturing industry in South Korea.

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16

Du, Yu-Xia, Ming-Jie Li, and Jun-Jie Huang. "Research on the emissions from industrial products exported from Guangdong Province—an input-output model analysis." PLOS ONE 17, no. 11 (November 3, 2022): e0276300. http://dx.doi.org/10.1371/journal.pone.0276300.

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This study uses an input-output model to analyze the wastewater, waste gas, and solid waste emissions in Guangdong’s industrial exports from 2004 to 2015; the Logarithmic Mean Divisia Index (LMDI) is used to analyze the factors influencing such pollution. The results reveal that embodied emissions of waste gas and solid waste in Guangdong’s export trade are increasing, while the increase in wastewater emissions is not apparent. The Logarithmic Mean Divisia Index (LMDI) is used to analyze the influencing factors of pollution, specifically, the structural, scale, and technical effects. We discovered that emissions of the top five industries account for about 80% of total emissions and the wastewater emissions’ technical effect has more impact; however, it is difficult for this technical effect in terms of embodied waste gas and solid waste to offset the scale and structural effects’ impacts. Moreover, the trends and factors influencing various industries’ pollution emissions differ. This study proposes that when the government carries out environmental pollution control measures, they should consider the embodied pollution caused by products from foreign trade and focus on treating industries with severe pollution. Simultaneously, the pollution controlling measures of different industries should also vary.

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17

Wang, Li Ning, Ding Ma, and Wen Ying Chen. "A New Improved Decomposition Model Based on Laspeyres Index: A Case of CO₂ Emission in China from 1996-2011." Advanced Materials Research 734-737 (August 2013): 1797–803. http://dx.doi.org/10.4028/www.scientific.net/amr.734-737.1797.

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Due to some drawbacks of the Divisia inedex and its relevant decomposition methods, such as difficult affording accurate estimations in period-wise and cross-region decompositions with large discrete change data and involving logarithmic calculations. This paper introduces a new decomposition index based on Laspeyres index (LI), leaving no unexplained residuals. It distributes interactive term among related factors according to different contribution rates of each factor which are related to the changes of these factors. An empirical illustration, decomposing changes in CO2emissions in China from 1996 to 2011 by both the proposed method (WLI) and Sun's Laspeyres index (SLI) methods time-series and period-wise methods, is carried out.

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18

Wood, Richard, and Manfred Lenzen. "Zero-value problems of the logarithmic mean divisia index decomposition method." Energy Policy 34, no. 12 (August 2006): 1326–31. http://dx.doi.org/10.1016/j.enpol.2004.11.010.

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19

Ang, B. W., and Na Liu. "Handling zero values in the logarithmic mean Divisia index decomposition approach." Energy Policy 35, no. 1 (January 2007): 238–46. http://dx.doi.org/10.1016/j.enpol.2005.11.001.

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20

Ang, B. W., and Na Liu. "Negative-value problems of the logarithmic mean Divisia index decomposition approach." Energy Policy 35, no. 1 (January 2007): 739–42. http://dx.doi.org/10.1016/j.enpol.2005.12.004.

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21

Choi, Ki-Hong, and B. W. Ang. "Attribution of changes in Divisia real energy intensity index — An extension to index decomposition analysis." Energy Economics 34, no. 1 (January 2012): 171–76. http://dx.doi.org/10.1016/j.eneco.2011.04.011.

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22

Hatzigeorgiou, Emmanouil, Heracles Polatidis, and Dias Haralambopoulos. "CO2 emissions in Greece for 1990–2002: A decomposition analysis and comparison of results using the Arithmetic Mean Divisia Index and Logarithmic Mean Divisia Index techniques." Energy 33, no. 3 (March 2008): 492–99. http://dx.doi.org/10.1016/j.energy.2007.09.014.

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23

Barnett, William A., Melvin J. Hinich, and Piyu Yue. "THE EXACT THEORETICAL RATIONAL EXPECTATIONS MONETARY AGGREGATE." Macroeconomic Dynamics 4, no. 2 (June 2000): 197–221. http://dx.doi.org/10.1017/s1365100500015030.

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In aggregation theory, index numbers are judged relative to their ability to track the exact aggregator functions nested within the economy's structure. We compare two statistical index numbers—the Divisia monetary aggregate and the simple-sum monetary aggregate—with the exact rational expectations monetary aggregate, using actual data. Because we are not using simulated data, we estimate the parameters of the Euler equations, and thereby of the nested monetary aggregator function, using the generalized method of moments. We explore the tracking errors of the two index numbers relative to the estimated exact aggregate. We investigate the circumstances under which risk aversion increases tracking error. We also use polyspectral methods to test for the existence of remaining nonlinear structure in the residual tracking errors.

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24

Barnett, William A., and Liting Su. "RISK ADJUSTMENT OF THE CREDIT-CARD AUGMENTED DIVISIA MONETARY AGGREGATES." Macroeconomic Dynamics 23, S1 (June 6, 2018): 90–114. http://dx.doi.org/10.1017/s1365100518000160.

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While credit cards provide transactions services, as do currency and demand deposits, credit cards have never been included in measures of the money supply. The reason is accounting conventions, which do not permit adding liabilities, such as credit card balances, to assets, such as money. However, economic aggregation theory and index number theory measure service flows and are based on microeconomic theory, not accounting. Barnett et al. derived the aggregation and index number theory needed to measure the joint services of credit cards and money. They derived and applied the theory under the assumption of risk neutrality. But since credit card interest rates are high and volatile, risk aversion may not be negligible. We extend the theory by removing the assumption of risk neutrality to permit risk aversion in the decision of the representative consumer.

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25

Feng, Guohua, and Apostolos Serletis. "A primal Divisia technical change index based on the output distance function." Journal of Econometrics 159, no. 2 (December 2010): 320–30. http://dx.doi.org/10.1016/j.jeconom.2010.09.006.

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26

Liang, Lingfeng, Xiancun Hu, Linda Tivendale, and Chunlu Liu. "The log mean divisia index based carbon productivity in the Australian construction industry." Construction Economics and Building 17, no. 3 (September 21, 2017): 68–84. http://dx.doi.org/10.5130/ajceb.v17i3.5544.

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Environmental protection and economic growth are two indicators of sustainable global development. This study aims to investigate the performance of environmental protection and economic growth by measuring carbon productivity in the construction field. Carbon productivity is the amount of gross domestic product generated by the unit of carbon emissions. The log mean Divisia index method is used to investigate influential factors including carbon intensity, energy intensity and regional adjustment that impact on changes of carbon productivity. The study utilises a range of data from the Australian construction industry during 1995-2004 including energy consumption, industry value added and carbon dioxide equivalent consumption. The research indicates carbon productivity in the Australian construction industry has clearly increased. Energy intensity plays a significant positive role in promoting carbon productivity, whereas carbon intensity and regional adjustment have limited influence. Introducing advanced construction machinery and equipment is a feasible pathway to enhance carbon productivity. The research method is generic and can be used to measure other performance indicators and decomposing them into influential factors.

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27

Ezer, Mehmet. "DO MONETARY AGGREGATES BELONG IN A MONETARY MODEL? EVIDENCE FROM THE UK." Buletin Ekonomi Moneter dan Perbankan 22, no. 4 (January 20, 2020): 509–30. http://dx.doi.org/10.21098/bemp.v22i4.1184.

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Conventional monetary models focus on interest rates and omit monetary aggregatesfrom policy discussions. This paper examines whether augmenting the measure ofmonetary policy with monetary aggregates helps determine more robust links betweenpolicy and economic fluctuations. After constructing the Divisia money index for theUK, I employ structural vector autoregression to identify two different UK monetarypolicy regimes. Inclusion of this (correct) measure of money and disentangling themoney supply from demand resolve the price and liquidity puzzles. The results pointto the informational content embedded in monetary aggregates, suggesting theyshould be taken into account in evaluations of monetary policy.

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28

Gillani, Syeda Fizza. "Elasticity and Buoyancy of Federal Taxes in Pakistan." Pakistan Development Review 25, no. 2 (June 1, 1986): 163–74. http://dx.doi.org/10.30541/v25i2pp.163-174.

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This paper focuses on the revenue-expenditure activities of the federal government and evaluates the performance of the fiscal system on the basis of estimates of revenue productivity. Two methodologies for the estimation of the short-run and long-run elasticity and buoyancy for tax revenue are evaluated. It is found that the Divisia Index method is superior on both theoretical and practical grounds and the results obtained are substantiated by the proportional - adjustment method. The study finds that the built-in elasticity of Pakistan's tax system was greater than unity.

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29

Li, Han, and Lin Wu. "Analysis of Hubei Province Industry’s Carbon Emissions Based on the LMDI." Applied Mechanics and Materials 675-677 (October 2014): 1865–68. http://dx.doi.org/10.4028/www.scientific.net/amm.675-677.1865.

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LMDI (Logarithmic Mean Divisia Index) was used to estimates the carbon emission of industrial energy consumption in Hunan Province with collected data on industrial energy consumption in 2000-2012. The results showed that carbon emissions of industrial energy consumption present the overall upward trend in Hubei Province, where the carbon emissions of coal consumption are the main factors, this shows that the industry of Hubei is extensive development withhigh energy consumption. In addition, industrial carbon intensity has a fluctuated downward trend in 2011-2012; this shows that Hubei province has made a positive change on control carbon emissions of energy consumption.

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30

Liu, Gengyuan, Yan Hao, Yun Zhou, Zhifeng Yang, Yan Zhang, and Meirong Su. "China's low-carbon industrial transformation assessment based on Logarithmic Mean Divisia Index model." Resources, Conservation and Recycling 108 (March 2016): 156–70. http://dx.doi.org/10.1016/j.resconrec.2016.02.002.

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31

Fernández González, P., M. J. Presno, and M. Landajo. "Regional and sectoral attribution to percentage changes in the European Divisia carbonization index." Renewable and Sustainable Energy Reviews 52 (December 2015): 1437–52. http://dx.doi.org/10.1016/j.rser.2015.07.113.

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32

Burney, Syed Muhammed Aqil, and Arfa Maqsood. "Influential Observations in Stochastic Model of Divisia Index Numbers with AR(1) Errors." Applied Mathematics 05, no. 06 (2014): 975–82. http://dx.doi.org/10.4236/am.2014.56093.

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33

Pan, Wei, Haiting Tu, Cheng Hu, and Wulin Pan. "Driving forces of China’s multisector CO2 emissions: a Log-Mean Divisia Index decomposition." Environmental Science and Pollution Research 27, no. 19 (April 15, 2020): 23550–64. http://dx.doi.org/10.1007/s11356-020-08490-0.

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34

Darvas, Zsolt. "Does money matter in the euro area? Evidence from a new Divisia index." Economics Letters 133 (August 2015): 123–26. http://dx.doi.org/10.1016/j.econlet.2015.05.034.

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35

Wang, Lanhui, Zichan Cui, Jari Kuuluvainen, and Yongyu Sun. "Does Forest Industries in China Become Cleaner? A Prospective of Embodied Carbon Emission." Sustainability 13, no. 4 (February 20, 2021): 2306. http://dx.doi.org/10.3390/su13042306.

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Forests and the forest products industry contribute to climate change mitigation by sequestering carbon from the atmosphere and storing it in biomass, and by fabricating products that substitute other, more greenhouse-gas-emission-intensive materials and energy. This study investigates primary wood-working industries (panel, furniture, pulp and paper) in order to determine the development of carbon emissions in China during the last two decades. The input–output approach is used and the factors driving the changes in CO2 emissions are analyzed by Index Decomposition Analysis–Log Mean Divisia Index (LMDI). The results show that carbon emissions in forest product industries have been declining during the last twenty years and that the driving factor of this change is the energy intensity of production and economic input, which have changed dramatically.

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36

Xiang, Xiwang, Xin Ma, Zhili Ma, Minda Ma, and Weiguang Cai. "Python-LMDI: A Tool for Index Decomposition Analysis of Building Carbon Emissions." Buildings 12, no. 1 (January 17, 2022): 83. http://dx.doi.org/10.3390/buildings12010083.

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Анотація:

A timely analysis for carbon emission reduction in buildings is an effective global response to the crisis of climate change. The logarithmic mean Divisia index (LMDI) decomposition analysis approach has been extensively used to assess the carbon emission reduction potential of the buildings sector. In order to simplify the calculation process and to expand its application scope, a new open-source Python tool (PyLMDI) developed in this article is used to compute the results of LMDI decomposition analysis, including multiplicative and additive decomposition. Users can quickly obtain the decomposition result by initializing the input data through a simple class data structure. In addition, the carbon emissions from commercial buildings are used as a numerical example to demonstrate the function of PyLMDI. In summary, PyLMDI is a potential calculation tool for index decomposition analysis that can provide calculation guidance for carbon emission reduction in the buildings sector. The data and codes for the numerical example are also included.

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37

Yang, Junliang, and Haiyan Shan. "Identifying Driving Factors of Jiangsu’s Regional Sulfur Dioxide Emissions: A Generalized Divisia Index Method." International Journal of Environmental Research and Public Health 16, no. 20 (October 19, 2019): 4004. http://dx.doi.org/10.3390/ijerph16204004.

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Анотація:

The Chinese government has made some good achievements in reducing sulfur dioxide emissions through end-of-pipe treatment. However, in order to implement the stricter target of sulfur dioxide emission reduction during the 13th “Five-Year Plan” period, it is necessary to find a new solution as quickly as possible. Thus, it is of great practical significance to identify driving factors of regional sulfur dioxide emissions to formulate more reasonable emission reduction policies. In this paper, a distinctive decomposition approach, the generalized Divisia index method (GDIM), is employed to investigate the driving forces of regional industrial sulfur dioxide emissions in Jiangsu province and its three regions during 2004–2016. The contribution rates of each factor to emission changes are also assessed. The decomposition results demonstrate that: (i) the factors promoting the increase of industrial sulfur dioxide emissions are the economic scale effect, industrialization effect, and energy consumption effect, while technology effect, energy mix effect, sulfur efficiency effect, energy intensity effect, and industrial structure effect play a mitigating role in the emissions; (ii) energy consumption effect, energy mix effect, technology effect, sulfur efficiency effect, and industrial structure effect show special contributions in some cases; (iii) industrial structure effect and energy intensity effect need to be further optimized.

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38

Wang, Shangjiu, Shaohua Zhang, and Liang Cheng. "Drivers and Decoupling Effects of PM2.5 Emissions in China: An Application of the Generalized Divisia Index." International Journal of Environmental Research and Public Health 20, no. 2 (January 4, 2023): 921. http://dx.doi.org/10.3390/ijerph20020921.

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Although economic growth brings abundant material wealth, it is also associated with serious PM2.5 pollution. Decoupling PM2.5 emissions from economic development is important for China’s long-term sustainable development. In this paper, the generalized Divisia index method (GDIM) is extended by introducing innovation indicators to investigate the main drivers of PM2.5 pollution in China and its four subregions from 2008 to 2017. Afterwards, a GDIM-based decoupling index is developed to examine the decoupling states between PM2.5 emissions and economic growth and to identify the main factors leading to decoupling. The obtained results show that: (1) Innovation input scale and GDP are the main drivers for increases in PM2.5 emissions, while innovation input PM2.5 intensity, emission intensity, and emission coefficient are the main reasons for reductions in PM2.5 pollution. (2) China and its four subregions show general upward trends in the decoupling index, and their decoupling states turn from weak decoupling to strong decoupling. (3) Innovation input PM2.5 intensity, emission intensity, and emission coefficient contribute largely to the decoupling of PM2.5 emissions. Overall, this paper provides valuable information for mitigating haze pollution.

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39

Balk, Bert M. "ON THE RELATION BETWEEN GROSS OUTPUT– AND VALUE ADDED–BASED PRODUCTIVITY MEASURES: THE IMPORTANCE OF THE DOMAR FACTOR." Macroeconomic Dynamics 13, S2 (September 2009): 241–67. http://dx.doi.org/10.1017/s1365100509090130.

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In this paper I consider the relation between gross output– and value added–based total factor productivity (TFP) measures. It appears that, without any (micro-)economic theory being required, a conditional relationship between TFP indices can be derived, in which the Domar factor plays an important role. At the same time it turns out that gross output– and value added–based TFP indicators (difference-type measures) always coincide. In the Divisia index framework and maintaining the classical assumptions (profit maximization and a production technology that exhibits globally constant returns to scale), it appears that both TFP indices measure technological change, albeit in a dual way. In establishing this result, no separability assumptions are involved. Both indices are in general path-dependent. Path independence of the gross output–based TFP index requires that the technology exhibit Hicks input neutrality, whereas path independence of the value added–based TFP index requires Hicks value-added neutrality. These two concepts of neutrality are, however, not dual.

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40

Zhou, Chun Yi, Yang Liu, and Ying Zi Li. "The Effect of Urban Aging on Human Capital." Applied Mechanics and Materials 651-653 (September 2014): 2430–34. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.2430.

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This paper uses the Chinese data of 1990-2012 and logarithmic mean Divisia index decomposition model to test the contribution of each factor on the human capital. This paper found the average annual growth rate of education and area are higher than age and gender; after analyzing the contributions, the contribution of education and area are still far higher than that of age and gender. Especially with the aging of population, the contribution of human capital was negative which inhibited the growth of human capital in our country. Therefore, to optimize the population structure is one of the most important measures to promote human capital and economic growth.

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41

Liu, Xiao Yu, Hai Lin Mu, Hua Nan Li, and Miao Li. "A Decomposition Analysis of Developing Low-Carbon Economy in Dalian Industry Sector." Advanced Materials Research 361-363 (October 2011): 1954–59. http://dx.doi.org/10.4028/www.scientific.net/amr.361-363.1954.

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In this paper, we utilize Logarithmic Mean Divisia Index (LMDI) techniques to decompose different components —CO2 emission factor, industrial energy mix, industrial energy intensity, industrial-scale structure, industrial structure, economic activity, family size and family households—which contribute to the changes in CO2 emissions in Dalian industry sector based on industry economy and CO2 emissions data in Dalian from 2000 to 2009. The results show that the economic activity was the main component for CO2 emissions increase, and energy intensity was the most favorable component in developing low-carbon economy in Dalian industry sector, and optimize energy mix could contribute to a significant reduction in CO2 emissions.

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42

Ma, Zhuo, Xiao Gang He, Xun Zhou Tong, Hai Yan Duan, Xian En Wang, and De Ming Dong. "The Study on Carbon Emission Influencing Factors of Industrial Energy Consumption of Changchun City." Applied Mechanics and Materials 164 (April 2012): 302–5. http://dx.doi.org/10.4028/www.scientific.net/amm.164.302.

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Анотація:

To make great efforts for energy saving and promote low-carbon transition of industrial development pattern have been the most crucial tasks for Changchun industrial developmen. Using Logarithmic Mean Divisia Index (LMDI) mode decomposes the carbon emission influencing factors of the industrial department in Changchun, and study on the effects of factors on the carbon emissions of industrial energy consumption. The result shows that the major factors for carbon emissions of industrial energy consumption in Changchun are economic development, the population size and the industrialization rate, and the key factors for the carbon emission changes in industrial department of Changchun are the energy consumption structure and the energy intensity.

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43

Fässler, Albert, and Arthur Vogt. "Analytic integration of the divisia price index on the exponential path for two commodities." Communications in Statistics - Theory and Methods 18, no. 9 (January 1989): 3473–76. http://dx.doi.org/10.1080/03610928908830103.

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44

Gazely, A. M., and J. M. Binner. "The application of neural networks to the Divisia index debate: evidence from three countries." Applied Economics 32, no. 12 (October 2000): 1607–15. http://dx.doi.org/10.1080/000368400418998.

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45

Hao, Yu, Lingou Wang, Weiyang Fan, Yaoyao Wei, Tong Wen, and Kun Zhang. "What determines China's electricity consumption? New evidence using the logarithmic mean Divisia index method." Journal of Renewable and Sustainable Energy 10, no. 1 (January 2018): 015909. http://dx.doi.org/10.1063/1.5003337.

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46

Wang, Yanqiu, Zhiwei Zhu, Zhaoge Zhu, and Zhenbin Liu. "Analysis of China's energy consumption changing using the Mean Rate of Change Index and the logarithmic mean divisia index." Energy 167 (January 2019): 275–82. http://dx.doi.org/10.1016/j.energy.2018.10.177.

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47

Guo, Mo, Jin-nan Wang, and Jun Bi. "Decomposition analysis of water consumption-related chemical oxygen demand emission in Chinese industrial sectors." Water Policy 16, no. 5 (April 16, 2014): 805–23. http://dx.doi.org/10.2166/wp.2014.028.

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Water shortage in China is caused by the uneven distribution of water resources, a situation that can worsen given overexploitation and pollution. Chemical oxygen demand (COD) emission is considered the most important water pollutant. Using the logarithmic mean Divisia index (LMDI) method, we analyse the main driving factors of the 2001–2011 changes in China's industrial water consumption-related COD emission. The main driving factors of COD emission are classified into five effects, namely, end-of-pipe treatment, COD emission intensity, water intensity, structural effect, and scale effect. In contrast to previous studies, the current work considers water consumption by using water intensity as an index. Results show that end-of-pipe treatment, with an effect of up to 35%, was the primary factor that influenced emission reduction during the studied period. Pollution reduction policies are key drivers of promoting advancements in technologies for reducing COD emission in industrial sectors, and technical efficiency in 2001–2011.

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48

Jiang, Xue-Ting, Min Su, and Rongrong Li. "Decomposition Analysis in Electricity Sector Output from Carbon Emissions in China." Sustainability 10, no. 9 (September 12, 2018): 3251. http://dx.doi.org/10.3390/su10093251.

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Анотація:

Carbon emissions from China’s electricity sector account for about one-seventh of the global carbon dioxide emissions, or half of China’s carbon dioxide emissions. A better understanding of the relationship between CO2 emissions and electric output would help develop and adjust carbon emission mitigation strategies for China’s electricity sector. Thus, we applied the electricity elasticity of carbon emissions to a decoupling index that we combined with advanced multilevel Logarithmic Mean Divisia Index tools in order to test the carbon emission response to the electric output and the main drivers. Then, we proposed a comparative decoupling stability analysis method. The results show that the electric output effect played the most significant role in increasing CO2 emissions from China’s electric sector. Also, “relative decoupling” was the main state during the study period (1991–2012). Moreover, the electricity elasticity of CO2 emissions had a better performance regarding stability in the analysis of China’s electricity output.

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49

Dias, Olen, and Alexander Vaninsky. "FACTOR ANALYSIS OF THE CO2 EMISSIONS IN THE UNITED STATES WITH IMPLICATIONS FOR THE ENVIRONMENTAL POLICY." JOURNAL OF SOCIAL SCIENCE RESEARCH 7, no. 3 (May 6, 2015): 1360–71. http://dx.doi.org/10.24297/jssr.v7i3.3588.

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Анотація:

This paper applies the Generalized Divisia Index to decompose the CO2 emissions into eight components and uses the Factor Analysis to determine their clusters - the combinations of related components that play the leading role. Economic analysis of these clusters allows for the determination of the main drivers of the CO2 emissions. As a case study, we used the data of the United States from 1950 through 2040 separated into three periods: 1950 - 1980; 1981 - 2012, and 2013 - 2040, each characterized by a specific type of socioeconomic development: industrial, post-industrial, and information, respectively. Data for the last period are projections. As a result, we got an insight into the typology of the CO2 emissions and obtained recommendations on environmental policy aimed at their mitigation.

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50

Eidukaitis, Simonas, and Tomas Balezentis. "Agricultural revenue in Latvia, Lithuania, and Poland: An application of the LMDI decomposition." JOURNAL OF INTERNATIONAL STUDIES 15, no. 3 (2022): 9–22. http://dx.doi.org/10.14254/2071-8330.2022/15-3/1.

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This paper analyses the trends and sources of change in agricultural revenue for the new European Union (EU) member states, namely Latvia, Lithuania, and Poland. These countries still lag behind the old EU member states in terms of profitability, productivity, and farm size. Thus, assessment of the patterns in agricultural revenue changes in these countries may provide evidence for reasonable policy making. The period covered is 2011-2019. The analysis is carried out for selected agricultural products (wheat, milk, poultry, potatoes). The logarithmic mean Divisia index is applied for the decomposition. The results indicate that the effects of scale, productivity, and price change varied across the countries and products. Therefore, dedicated policy measures and pathways are needed for each particular context.

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