Constructing Time-Series Input-Output Systems and Exploring Carbon Development Measures: From Retrospect to Prospect

Tian, Jing (2020) Constructing Time-Series Input-Output Systems and Exploring Carbon Development Measures: From Retrospect to Prospect. Doctoral thesis, University of East Anglia.

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Abstract

Input-output table (IOT) and social accounting matrix (SAM) are two interconnected but unique input-output systems in the area of economy. Constructing time-series input-output tables (IOTs) and social accounting matrices (SAMs) fulfils two tipping points. One is concerned with tracing the structural trajectories of economic systems from the past to the future. The other is with regard to rendering economic systems analytical dynamically and effectuating the explorations of various external variables from the time dimension. Further, environmentally extending time-series IOTs and SAMs with newly proposed techniques could be considered as meaningful feedbacks for the development of time-series input-output systems.

Favourable endeavours have been devoted to constructing time-series IOTs and SAMs, and, correspondingly, to exploring carbon development measures. However, increasing attention could be drawn to the role of utilizing and exploiting the fundamental and important features of input-output systems in achieving the aforementioned objectives. This consideration promotes the methods improvements of the five chapters (Chapters 2 to 6) of this thesis.

(1) Updating time-series input-output tables with economic structure concerns and identifying CO2 clusters changes. Time-series input-output tables (IOTs) elaborate economic structures over time. In this study, we therefore utilize economic structure concerns to update time-series IOTs. A new matrix calculation method is proposed for tracking and establishing matrix-based links among intermediate input-outputs, final demand and value added. The method is reinforced by reflecting price fluctuations in IOTs. This method is further extended by proposing a matrix-based linking method to trace the structure changes of final demand and value added. The validation analysis of time-series IOTs is conducted using Monte Carlo simulations in the context of the matrix-based structures of IOTs. Based on the time-series IOTs, CO2 clusters changes from production, consumption and income perspectives are identified, deriving sector characteristics to reduce CO2 emissions. This study is in the case of China from 1997 to 2020.

(2) A forward-backward realization of solutions to time-series social accounting matrices construction, validation and applications. Social accounting matrix (SAM) elucidates the economic transactions flowing forward and backward, thereby forming a matrix-based structure. This feature is exploited, constituting a forward-backward realization of solutions to time-series SAMs construction, validation and applications. In this study, the matrix-induced structure features time-series SAMs construction, during which K-nearest-neighbour algorithm and leave-one-out cross-validation are joint to handle missing data. Also, a new matrix calculation method is proposed to conduct time-series SAMs validation in terms of gauging the economy-wide effects of each economic agent. Using time-series SAMs, both demand- and supply-driven CO2 emissions are analysed and compared by extending multiplier decomposition analysis and structural path analysis. This study is in the case of China from 1997 to 2020.

(3) Input-output forecasting and CO2 inventories construction using a new subsystem decomposition analysis. Forecasting input-output tables and social accounting matrices is an attempt to trace the trends inherent in the economic system, and to render the economic system analytical when exploring the future of various external variables. In this study, we therefore propose a procedure of input-output forecasting. During this procedure, the input-output table series are forecasted by proposing an element-based Fourier-Markov method, then structured through modified matrix transformation technique and T-accounts concept, and last, validated by combining matrix calculation methods with Monte Carlo simulations. On the basis of the forecasted table series, we construct CO2 inventories by proposing a new integrated method, that is, the combination of subsystem analysis with structural decomposition analysis. With this method, CO2 inventories quantify historical and future emission channels throughout the economic system from demand and supply sides, and then account for the contributions of influencing factors behind temporal changes in emission channels. This study is in the case of China from 1997 to 2025.

(4) An integrated scheme of input-output future scenarios construction interconnecting production with consumption and sector-level CO2 emissions synergistic alleviation. In response to the explorations of prospective trajectories, input-output analysis (IOA) in a scenario context could encompass intra- and inter-sector linkages in future scenarios, and also investigate the potential pathways of external variables. When integrating IOA and scenario analysis, multi-criteria decision making techniques have found to be feasible and useful. Against this backdrop, we propose an integrated scheme of input-output future scenarios construction to help alleviate CO2 emissions in a holistic manner. That is, we set up three categories of input-output future scenarios by interconnecting production with consumption. In detail, we start input-output BAU scenario through a procedure of input-output forecasting. We then construct input-output policy-related scenario by extending the multi-objective optimization method with multiple policy-related parameters. We finally arrange input-output problem-specific scenarios using multi-attribute decision making which incorporates the generalized weighting method into the permutation and combination method, and supports the construction of a multi-attribute importance method. Within the three constructed categories of input-output future scenarios, sector-level CO2 emissions synergistic alleviation is analysed. This study is in the case of China from 2020 to 2030.

(5) Input-output future configuring system for low-carbon economy using a social accounting matrix optimization design. To reflect and reconcile the future trends in the context of low-carbon economy (LCE), this study proposes an input-output future configuring system for LCE. This system is constructed by a social accounting matrix optimization design, which is achieved within the computable general equilibrium (CGE)-based framework. During the system construction, optimum designs are conducted for LCE performance and planning-based parameter setting. Then, a CGE appraisal framework is integrated into optimization. Also, this system is validated through Monte Carlo simulations. When applying this system to the LCE context, a new method is proposed through combining the element-based Fourier-Markov method with the simultaneous equations method, so as to analyze the impacts of future trends on economy, energy and CO2 emissions are analyzed and suggest countermeasures correspondingly. This study is in the case of China from 2020 to 2030.

Item Type:	Thesis (Doctoral)
Faculty \ School:	Faculty of Social Sciences > School of Global Development (formerly School of International Development)
Depositing User:	Kitty Laine
Date Deposited:	30 May 2025 14:44
Last Modified:	30 May 2025 14:44
URI:	https://ueaeprints.uea.ac.uk/id/eprint/99370
DOI:

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