Carbon performance of firms in developing countries: The role of financial slack, carbon prices and dense network

Ashraf, Naeem; Comyns, Breeda; Tariq, Syed; Chaudhry, Hassan Rauf

doi:10.1016/j.jclepro.2019.119846

Cited by 18 publications

(12 citation statements)

References 86 publications

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“…However, they produced mixed findings and consensus has not yet been reached on the nature of this relationship. Ashraf et al's (2020) analysis of South Asian cement manufacturing firms demonstrates that financial slack generates a positive relationship with carbon performance, and that this link is moderated through carbon prices in a negative direction. However, company density was found to positively moderate this association.…”

Section: Literature Reviewmentioning

confidence: 99%

Carbon performance, company financial performance, financial value, and transmission channel: an analysis of South African listed companies

Ganda

2022

Environ Sci Pollut Res

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This article examines the influence of carbon performance on corporate financial performance and company financial value among South African listed firms for the period 2014 to 2018 using a two-step GMM panel process. The short-run findings show that carbon performance develops a positive and significant association with return on assets, firm value and Tobin's Q. In the long run, the relationship between carbon performance and return on assets as well as firm value is significantly negative; however, the link with Tobin's Q remains positively significant. Where carbon performance is employed as the dependent parameter, a positive, significant relationship is established with return on assets, firm value and Tobin's Q in both the short and long run. The findings also demonstrate that carbon performance is a transmission channel whereby the debt-toequity ratio, interest cover ratio, price to cash flow ratio and current ratio improve corporate financial performance and firm value in the long run. In the short run, the regression analysis frameworks produce mixed findings on whether carbon performance is a transmission channel.Policy recommendations are made based on the findings.

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Section: Literature Reviewmentioning

confidence: 99%

Carbon performance, company financial performance, financial value, and transmission channel: an analysis of South African listed companies

Ganda

2022

Environ Sci Pollut Res

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“…As an important tool for climate mitigation, how to improve carbon emission performance is a key academic concern. According to previous studies, economic development, industrial structure, government intervention, the level of financial development and the level of foreign investment are the key factors influencing carbon emission performance (Magazzino, 2016;Ashraf et al, 2020;Song et al, 2021;Li L et al, 2022;.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To assess the impact of ICP on urban carbon emission performance more reliably, this paper incorporates a series of control variables in the model, including the level of economic development (lnrgdp), industrial structure (is), government intervention (gov), the level of financial development (fin) and the level of foreign investment (fdi) (Magazzino, 2017b;Ashraf et al, 2020;Song et al, 2021;Wang and Huang, 2022;Wang S et al, 2022;Wang W et al, 2022;Wu D et al, 2022). The specific measures of each variable are shown in Table 1.…”

Section: Control Variablementioning

confidence: 99%

Assessing the effectiveness of innovative city pilots in improving urban carbon emission performance: A spatial difference-in-difference approach

Long

Tan³

et al. 2022

Front. Environ. Sci.

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Existing studies have focused on the impact of innovation on carbon emission performance but ignore the importance of government support for innovation. To overcome this challenge, this paper adopts a spatial difference-in-difference (DID) model to assess the impact of government support for innovation on urban carbon emission performance based on a quasi-natural experiment of innovative city pilots (ICP) in China. Using the high-resolution carbon emission data of 1 km × 1 km for 238 cities from 2008 to 2019 in China, this paper employees an extended stochastic frontier analysis (SFA) model to measure urban carbon emission performance. Our findings indicate that ICP implementation leads to a 1.3% improvement in local carbon emission performance. Meanwhile, there is a significant spatial spillover effect of ICP implementation, with a 3.3% improvement in the carbon performance of the surrounding areas. The results of the mechanism analysis suggest that government innovation support affects carbon emission performance by promoting total factor productivity improvement, green innovation, and industrial upgrading. Further analysis shows that ICP has the strongest impact on carbon performance in the eastern region, and the impact is stronger for large cities and resource-dependent cities. Finally, the paper carries out a series of robustness tests to ensure the reliability of the analytical results, including parallel trend tests, placebo tests and re-estimation of different methods. Based on the findings, this paper proposes feasible policy recommendations in terms of continuous promotion of government innovation support, regional cooperation and differentiated innovation support formulation.

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“…Secondly, they did not consider heterogeneity of the direct and spillover effects across space and over time (Li et al 2019 ). Furthermore, they did not take into account that energy price may have a determined negative effect on energy consumption (Ren et al 2009 ; Du 2019 ; Ashraf et al 2020 ; Wang et al 2020c ; Neya et al 2020 ).…”

Section: Introductionmentioning

confidence: 99%

The analysis of spatial–temporal effects of relevant factors on carbon intensity in China

Zheng

Long

Fan

2022

Stoch Environ Res Risk Assess

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The increasing carbon emissions have been a major concern for most countries around the world. And as a result, every country is concerned about developing appropriate strategies to curtail it. As a major economy and the largest carbon emitter in the world, China has pledged to reduce the carbon intensity by 60–65% by 2030, compared with 2005 levels, and achieve carbon neutrality before 2060. Therefore, the analysis of the impact of China’s carbon intensity is becoming an increasing important topic. Due to the spatial heterogeneity of carbon intensity, various spatial econometric models have been applied in this field. However, the existing literatures failed to consider the cross-products of relevant factors. This paper constructs our dynamic general nesting spatial panel model (GNS) with common factors to deal with the dilemma, and examines the direct and spatial–temporal spillover effects of industrial structure, GDP per capita, investment in anti-pollution projects as percentage of GDP and energy price on carbon intensity in China over the period 2003–2017. Our analysis shows that: (1) China’s carbon intensity showed the spatial agglomeration and temporal “inertia” from 2003 to 2017. (2) From the time dimension, the long-term effect of industrial structure first increased and then gradually decreased. (3) From the spatial dimension, industrial structure and investment in anti-pollution projects as percentage of GDP accounted for the main spatial heterogeneity. Furthermore, this paper attempts to provide policy implications to help reduce carbon intensity and achieve carbon neutrality in China.

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Carbon performance of firms in developing countries: The role of financial slack, carbon prices and dense network

Cited by 18 publications

References 86 publications

Carbon performance, company financial performance, financial value, and transmission channel: an analysis of South African listed companies

Carbon performance, company financial performance, financial value, and transmission channel: an analysis of South African listed companies

Assessing the effectiveness of innovative city pilots in improving urban carbon emission performance: A spatial difference-in-difference approach

The analysis of spatial–temporal effects of relevant factors on carbon intensity in China

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