Elsevier

Technovation

Volume 110, February 2022, 102350
Technovation

Technological innovation, industrial structural change and carbon emission transferring via trade-------An agent-based modeling approach

https://doi.org/10.1016/j.technovation.2021.102350Get rights and content

Highligths

  • This paper introduces an agent-based modeling approach within an input-output framework.

  • This paper provides a new perspective to investigate the impacts of technological innovation.

  • There are two paths on how technological innovation affects the transfer of carbon emissions.

  • A more targeted regional economic governance measure is proposed.

Abstract

By introducing an agent-based modeling approach, this paper proposes the diffusion of technological innovation behaviors in simulation models. On this basis, a mass of heterogeneous firms within an input-output analytical framework are included to simulate policy scenarios in order to explore how firms' technological innovation affects industrial structural change, resulting in carbon emission transferring via trade (ETT), in the major global economies (i.e., China, Japan, the United States, Russia, India, and the European Union) between 2007 and 2030. One important finding is that there are sectoral and regional differences regarding the impacts of two types of technological innovation (i.e., product innovation and process innovation) on industrial structural adjustment and ETT. Moreover, compared to process innovation, product innovation is more conducive to lessening the impacts of trade on regional economies’ emissions. Another important finding is that the volume of ETT for these economies will continue to increase from 2012 to 2030, indicating that with the acceleration of regional industrial structural adjustment, the impacts of trade on regional carbon emissions accounting and reduction obligation assignments are potentially aggravating. Overall, this paper provides a new perspective for researchers to investigate industrial structural change and the resulting ETT. Moreover, to promote regional economic growth and tackle global climate change, this paper also gives policy makers the theoretical foundation to formulate more targeted regional economic governance measures.

Introduction

According to the Statistical Review of World Energy in 2018, which was released by the British Petroleum Company, with the rapid economic growth in various countries and regions, global demand for fossil fuels continued to increase at an annualized rate of approximately 2 %. Thus, the world is facing environmental pollution and ecological imbalance caused by pollutants from anthropogenic activities. Consequently, ecological environmental governance has become a major issue faced by stakeholders all over the world during the process of economic development (Biermann et al., 2017). To adapt to climate change by lessening ecological environmental risks, addressing climate change has entered an era of global governance. In particular, an important and unprecedented agreement (i.e., the Paris Protocol) regarding reducing pollutant emissions has been carried out since 2016 to strengthen the response to the threat of climate change. From a global environmental governance perspective, the key to implement the above agreement is that while each country in the world is committed to actively pursuing the shared targets of emissions reduction, they should also promote industrial structural adjustment and facilitate economic growth (Guan et al., 2018; Ozcan et al., 2020).

However, with the rapid development of the global economy, while trade played an important role in promoting regional economic development by providing a mechanism to efficiently allocate resources such as energy and capital, carbon emissions transferring via trade (ETT), including emissions embodied in imports (EEI) and emissions embodied in exports (EEE), occurred accordingly (Mi et al., 2019; Zheng et al., 2020). In other words, the geographical separation of consumers and producers (i.e., pollutant emissions released during the production of consumable goods) was also caused by international trade (Zhu et al., 2018; Mi et al., 2019). As a result, a mass of pollutant emissions shifted from consumers to pollution emitted during the production of consumable products, resulting in an increase of ETT (Lin et al., 2016; Shao et al., 2018). As for the imputation of obligations for pollutant emissions, consumers could evade their own reduction responsibility allocation through international trade, which inevitably lowered emissions reduction efficiency. Moreover, for producers, it would also have a negative influence on participation in global emissions reduction. In particular, with the rapid development of global economic integration, ETT is becoming increasingly serious; the corresponding volume increased by nearly 80 % from 1995 to 2007 (Sato, 2014; Lenzen, 2016), and thus for each country, international trade also has a more significant impact on regional emissions accounting and the imputation of obligations for emissions. Under the constraint of the global emissions reduction target, to assign pollutant emissions reduction obligations to each country equitably, an economic governance measure considering ETT is becoming attractive to policy makers (Meng et al., 2018; Mi et al., 2019; Sun et al., 2020). Consequently, many scholars have paid attention to ETT and the relevant issues at the global level.

Notably, however, previous studies found that participating in global emission reduction would inevitably bring about a corresponding reduction in costs (Nordhaus, 2015). Thus, it would lead to a significant increase in economic and social development uncertainties for all regions in the world and further hinder the sustainable growth of the global economy (Riahi et al., 2015; Rogelj et al., 2018). More seriously, it would ultimately hurt enthusiasm in various regions for participating in reducing pollutant emissions and potentially restrict the development space of regional economies in the future. This might be one of the major reasons that the United States withdrew from the Paris Agreement in 2017. In this context, the motivation of this paper is to develop more targeted regional economic governance policies to improve industrial structural adjustment and boost economic growth, while we actively curtail emissions in response to global climate change.

In fact, technological innovation has received considerable attention as being an effective measure to address the challenge of global climate change and promote the transformation and upgrade of regional industrial structure (Cheng et al., 2017; Liao et al., 2020). However, from the perspective of regional economic governance, if innovation only occurs in individual regions and the resulting technological progress is only possessed by a few regions, it is not in line with the trend of regional economic integration (Stummer et al., 2015; Zanello et al., 2016). To achieve the goal of promoting global economic growth and the emissions reduction target, it is urgent that policy makers promote technological innovation and the resulting technological progress, and improve the level of overall technological innovation. Therefore, accelerating technological innovation and its spatial diffusion has become an inevitable requirement of global emissions reduction and industrial structural adjustment (Bloom et al., 2016; Janger et al., 2017; Fried, 2018). In particular, with global economic development transforming from a resource-driven system that relied excessively on traditional fossil energies into a technology-driven economy characterized by low carbon and green economic development strategies, for policy makers in each country, strategies for fully utilizing technological innovation to promote the transformation and upgrade of industrial structure and tackle global climate change are of great importance. It would not only have a profound impact on regional economic development, it would also have a significant influence on global climate governance. Thus, it raises an increasingly challenging question: How does technological innovation affect regional industrial structural change and ETT? This is a vital issue that researchers urgently need to address.

In the existing literature, relevant studies on the impacts of technological innovation on industrial structural change and ETT can be divided into the following three primary types.

The first type placed emphasis on exploring the impact of technological innovation or industrial structure on energy consumption and the resulting pollutant emissions based on the econometric modeling and computational general equilibrium modeling methods (Carrión-Flores et al., 2010; Corradini et al., 2014; Irandoust, 2016; Yahoo and Othman, 2017; Wu et al., 2019; An et al., 2020). In general, they found that promoting technological innovation and the resultant industrial structural adjustment was conducive to reducing the consumption of fossil energy. Although prior studies provided an important reference for policy makers to develop effective emission reduction policies, less emphasis was placed on the effect of technological innovation on ETT. However, with global trading relations becoming increasingly close, the pace of industrial structural adjustment in various regions is accelerating, and thus ETT between regions is becoming more and more serious (Meng et al., 2018; Mi et al., 2019; Zheng et al., 2020). It would not only lower pollution reduction efficiency, it would also have a negative impact on promoting joint participation from all regions in pollutant emissions reduction. Thus, a huge number of scholars have paid more attention to investigating ETT-related issues, and the relevant literatures could be summarized in the following two types.

The second type focused on accounting for ETT and related issues. Specifically, using input-output (IO) models, a mass of scholars primarily estimated the volume of ETT and investigated the related pollutant emission reduction policies on the global scale (Chen and Chen, 2011; Meng et al., 2018; Liu et al., 2020), the national scale (Arce et al., 2016; Wang et al., 2014; Escobar et al., 2020), and the city scale (Feng et al., 2014; Mi et al., 2016, 2019; Zhong et al., 2020). Overall, these authors found that with widespread economic globalization and regional economic integration contributing unceasing aggravation, ETT was on the rise, and thus the geographical separation of production and consumption tended to be more and more serious during the studied period. Additionally, carbon emissions volumes primarily flowed from underdeveloped regions to relatively developed regions. Furthermore, for some advanced economies, by relocating the production of products abroad, consumers evaded some responsibility for pollutant emissions reduction. For some relatively backward economies, international trade provided a mechanism to effectively assign a mass of resources to boost local economic growth, but as a result of these strong trade relationships, these regions would be left to shoulder more responsibility for emission reduction, which should receive more attentions.

The third type paid more attention to analyzing determinants influencing ETT; that is, by employing social network analysis methods, structural decomposition methods, and econometric regression models within an IO framework, respectively, a number of previous studies examined the impacts of a series of important factors (i.e., emission intensity, demand for final products, energy structure, industrial structure, and regional economic development level) on ETT and put forward corresponding emission reduction policies (Xu and Dietzenbacher, 2014; Shahbaz et al., 2017; Jiang et al., 2019; Zheng et al., 2020). Generally, the increase in EEE was mainly influenced by the change in the demand in other regions, whereas the growth of EEI was primarily determined by requirements from the local region and production in other regions. Moreover, optimizing the energy consumption structure and increasing the share of clean energy in the energy consumption structure would significantly lessen ETT, while the continuous increase of per capita income would stimulate residents’ consumption demand to increase the need for external products and services.

In sum, although the literature on the impacts of technological innovation and industrial structural change on pollutant emissions as well as the ETT-related issues has developed considerably over the past decades, it remains largely fraught by two limitations that we seek to improve in this paper.

First, few studies focused on the possible impacts of different types of technological innovation on ETT. By choosing some substitution variables (i.e., the number of patent applications and authorizations or the expenditure on research and development (R&D) in a region or industry) as representative of technological innovation, although a few prior studies (Carrión-Flores and Innes, 2010; Corradini et al., 2014; Zhang et al., 2020) investigated the influence of technological innovation on energy consumption and the resulting pollutant emissions, different types of technological innovation are less discussed here. In fact, considering that innovation related to firms' production technology played an important role in promoting regional economic growth, classic innovation theory asserts that innovation is primarily composed of technological innovation, containing two different innovative modes, i.e., product innovation and process innovation (Schumpeter, 1912; Nelson and Winter, 1982; Lorentz and Savona, 2010). Furthermore, product innovation could be divided into three types, i.e., self-dependent innovation, purchase of innovation, and imitative innovation (Wang et al., 2014; Wang and Chen, 2020). Since technological innovation has prominent regional attribution, the impacts on industries' and residents’ final demand would vary in different regions (Lorentz and Savona, 2008; 2010). Moreover, previous studies have shown that regional energy consumption resulting from different types of technological innovation would also differ in various regions (Lee et al., 2006; Nesta et al., 2014). Therefore, to develop effective regional economic governance policies, it is essential for stakeholders to examine the influence of different types of technological innovation on ETT.

Second, studies on the microscopic mechanism regarding the impacts of technological innovation on regional industrial structural change and ETT have not yet received attention. Using statistic models, econometric regression models, and computational general equilibrium (CGE) models, previous studies analyzed how technological innovation affected regional energy intensity and the resulting pollutant emissions, and performed in-depth analysis on how to formulate feasible corresponding measures (Cheng et al., 2017; Miao et al., 2019; Hamamoto, 2020). Nevertheless, prior studies have paid more attention to examining the related influence mechanism at the macro level, while analysis of the mechanism at the micro level is still insufficient. Additionally, these studies primarily focused on regional or industrial carbon emissions, rather than ETT. Notably, by means of structural decomposition models and spatial econometric regression models within an IO analytical framework, Xu and Dietzenbacher, 2014 and Jiang et al. (2019) investigated the impact of regional production technology and final demand structure on ETT, but they lacked discussions on the mechanism regarding the impacts of technological innovation on industrial structural change and ETT. In fact, prior studies found that promoting technological innovation might accelerate industrial structural change (Borghesi et al., 2015; Howell, 2020), and thus it could alleviate the increase in energy consumption and the resulting carbon emissions caused by the growth of regional residents’ consumption.

However, from a firm perspective, how does technological innovation affect ETT while promoting regional industrial structural adjustment? Technological innovation is essentially a microscopic individual process (Crowley and McCann, 2017), and firms are not only the primary micro-individuals for performing technological innovation in a market environment, they are also a key enabler to drive regional industrial structural change (Wang et al., 2014; Boschma, 2015). More importantly, regional industrial structural change is also a macro-emergent result from the interactions of technological innovation by a large number of heterogeneous firms at the micro level (Lorentz and Savona, 2010; Capello and Lenzi, 2013; Olk and West, 2020), and industrial structural change will lead to changes in ETT. In addition, being an extremely complex system, the dynamic changes of regional industrial structure and the resulting ETT in various regions are primarily driven by firms’ economic behaviors, including innovation activities and remarket demand (Isaksen, 2014). In fact, these elements are closely related, and they interact; hence, a typical bottom-up dynamic complex adaptive system is formed (Wang et al., 2014). Therefore, introducing an agent-based modeling (ABM) method might be a breakthrough point to remove the two limitations mentioned above.

Originating from a computer science paradigm called object-oriented programming, the ABM method began in the earliest research studies and was usually employed to investigate the simulation of social science behaviors around the 1980s (Schelling, 1978; Axelrod, 1987). Specifically, it has seen swift development and widespread application in many complex fields, including geography and economics, since the 1990s (Bonabeau, 2002). With the advancement in computer science and artificial intelligence technology in recent years, the ABM approach has become the third most important approach in scientific research, besides the traditional inductive and deductive methods (Axelrod and Tesfatsion, 2006). The most classic studies on the application of the ABM method perform the related policy simulation analysis for a series of complex socio-economic phenomena, such as predicting the spatial spread of infectious diseases, facilitating cultural communication, and promoting knowledge spillovers between regions (Epstein and Axtell, 1996).

In the present study, an agent is a collection of autonomous decision-making entities, and it can recognize a changing environment and other agents through agent communication and interaction (Parsons and Wooldridge, 2002). Different from the traditional equation-based modeling method, being a major bottom-up tool, ABM consists of a mass of heterogeneous agents who can execute various economic behaviors, such as producing, consuming, selling, and purchasing, thereby interacting with and affecting each other, learning from other agents’ experiences (including their own), assessing the situation, and making decisions, so that they are better suited to the environment on the basis of a set of rules (Wooldridge and Jennings, 1995; An, 2012). Complex adaptive systems can be characterized by interdependencies, heterogeneity, and nested hierarchies among the various types of agents and their respective economic environments (Nikolai and Madey, 2009). Therefore, by means of this method, a macroscopic complex economic issue can be simplified and abstracted as a sub-system consisting of interacting and autonomous agents with simple rules, and it can illustrate complex behavioral patterns and provide valuable insights regarding the dynamic change of the real-world economic structural system that it emulates. In the sub-system, many heterogeneous agents with bounded rationality may be capable of dynamic interaction, thus evolving and allowing unanticipated behaviors to emerge when making decisions (Manson, 2006; An, 2012). In particular, ABM is not only specialized in depicting the microscopic mechanism of competitive interactions between agents and modeling dynamic changes in a complex economic structure system, it is also appropriate for depicting a complex economic adaptive system with fundamental features such as dynamism, nonlinearity, and path dependence (Boschma and Frenken, 2006).

Compared to statistical models, econometric regression models, and CGE models, by modeling the emergent phenomena resulting from the interactions of abundant heterogeneous agents to exhibit dynamic changes in a complex macroeconomic structural system, ABM can be better used to examine the effect of changes on external conditions, like technological innovation policies on individual entities at the micro level (Ausloos et al., 2015). Therefore, applications of the ABM method have spanned a broad range of areas, such as modeling residents' behavior in the stock market and financial marketplace (Paulin et al., 2018; Petrović et al., 2020), modeling farmers' investment decisions in an agricultural region (Maes and Van Passel, 2017; Yamashita and Hoshino, 2018; Mack et al., 2020), and understanding consumers’ purchasing behavior (Rai and Henry, 2016; Sturley et al., 2018; Westphal and Sornette, 2020).

To address the two aforementioned limitations, using an ABM approach within an IO analytical framework, this paper proposes the diffusion of technological innovation behaviors in simulation models. On this basis, a large number of heterogeneous firms with an IO analytical framework are included to perform policy simulation scenarios in order to explore how firms' technological innovation affects industrial structural change and ETT in the major global economies (i.e., China, Japan, the United States, Russia, India, and the European Union (EU)) during the period 2007–2030. Regrettably, however, there is still a lack of relevant studies on this aspect. Therefore, this paper contributes to filling this gap by introducing the ABM method within IO models to discuss how firms’ technological innovation affects ETT while promoting the adjustment of regional economic structure. In summary, this paper will provide a new perspective for researchers to explore regional industrial structural change and ETT. More importantly, to facilitate economic growth and combat climate change, this paper can provide the theoretical foundation for policy makers to formulate more targeted regional economic governance measures.

The rest of this paper is organized as follows. Section 2 provides the related theoretic analysis framework. The model and data are given in Section 3. Section 4 discusses the simulation results of the ABM approach within an IO analytical framework. Finally, conclusions and implications will be presented in Section 5.

Section snippets

Theoretic analysis framework

To clarify the microscopic mechanism in relation to the impacts of technological innovation on regional industrial structural change and ETT, this paper proposes a theoretical analysis framework from the macro and micro level perspectives.

First, at the macro level (Fig. 1), the global economy contains a number of individuals (including countries and regions) with different industrial structure characteristics; each individual also consists of many industrial sectors. Specifically, the whole

An input-output analytical framework at the macro level

According to a series of prior studies conducted by Leontief (1951, 1970) and Lorentz and Savona (2008, 2010), based on an IO analytical framework, the sectoral output of each sector j in year t in a region can be decomposed into three components, namely, intermediate consumption (Ij,t), final domestic consumption (Cj,t), and net foreign final consumption (Xj,tMj,t) including export consumption and import consumption. Therefore, the overall output in a country is a function of the national

Scenario setting

Considering that two important modes of technological innovation played a significant role in jointly promoting the dynamic evolution of regional economic structure (Lorentz and Savona, 2008; 2010), the hybrid technological innovation-driven scenario (i.e., firms’ technological innovation contains process innovation and product innovation) is regarded as the baseline scenario here and is referred to as Scenario 3. The control scenarios are that firms only implement process innovation (Scenario

Conclusions

This paper aims to provide a new perspective for researchers to investigate industrial structural change and the resulting ETT. More importantly, to facilitate regional economic growth and combat global climate change, this paper can provide the theoretical foundation for policy makers to formulate more targeted regional economic governance measures. To this end, by introducing an ABM approach, this paper proposes the diffusion of technological innovation behaviors in the simulation models. On

Acknowledgments

The authors are grateful for the financial support provided by the National Natural Science Foundation of China under Grant No. 41801118, 71874185, and China Postdoctoral Science Foundation under Grant No. 2019M662017, and the Soft Science Research Program of Zhejiang Province under Grant No. 2020C35037.

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