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This paper aims to explore the pathways of carbon transfer in 200 US corporations along with the motivations that drive such transfers. The particular focus is on each firm’s embeddedness in the global value chain (GVC) and the influence of environmental law, operational costs and corporate social responsibility (CSR). The insights gleaned bridge a gap in the literature surrounding GVCs and corporate carbon transfer.

The methodology comprised a two-step research approach. First, the authors used a two-sided fixed regression to analyse the relationship between each firm’s embeddedness in the GVC and its carbon transfers. The sample consisted of 217 US firms. Next, the authors examined the influence of environmental law, operational costs and CSR on carbon transfers using a quantitative comparison analysis. These results were interpreted through the theoretical frameworks of the GVC and legitimacy theory.

The empirical results indicate positive relationships between carbon transfers and GVC embeddedness in terms of both a firm’s position and its degree. From the quantitative comparison, the authors find that the pressure of environmental law and operational costs motivate these transfers through the value chain. Furthermore, CSR does not help to mitigate transfers.

The findings offer insights for policymakers, industry and academia to understand that, with globalised production and greater value creation, transferring carbon to different parts of the GVC – largely to developing countries – will only become more common. The underdeveloped nature of environmental technology in these countries means that global emissions will likely rise instead of fall, further exacerbating global warming. Transferring carbon is not conducive to a sustainable global economy. Hence, firms should be closely regulated and given economic incentives to reduce emissions, not simply shunt them off to the developing world.

Carbon transfer is a major obstacle to effectively reducing carbon emissions. The responsibilities of carbon transfer via GVCs are difficult to define despite firms being a major consideration in such transfers. Understanding how and why corporations engage in carbon transfers can facilitate global cooperation among communities. This knowledge could pave the way to establishing a global carbon transfer monitoring network aimed at preventing corporate carbon transfer and, instead, encouraging emissions reduction.

This study extends the literature by investigating carbon transfers and the GVC at the firm level. The authors used two-step research approach including panel data and quantitative comparison analysis to address this important question. The authors are the primary study to explore the motivation and pathways by which firms transfer carbon through the GVC.

In the big data era, image compression is of significant importance in today’s world. Importantly, compression of large sized images is required for everyday tasks; including electronic data communications and internet transactions. However, two important measures should be considered for any compression algorithm: the compression factor and the quality of the decompressed image. In this paper, we use Frei-Chen bases technique and the Modified Run Length Encoding (RLE) to compress images. The Frei-Chen bases technique is applied at the first stage in which the average subspace is applied to each 3 × 3 block. Those blocks with the highest energy are replaced by a single value that represents the average value of the pixels in the corresponding block. Even though Frei-Chen bases technique provides lossy compression, it maintains the main characteristics of the image. Additionally, the Frei-Chen bases technique enhances the compression factor, making it advantageous to use. In the second stage, RLE is applied to further increase the compression factor. The goal of using RLE is to enhance the compression factor without adding any distortion to the resultant decompressed image. Integrating RLE with Frei-Chen bases technique, as described in the proposed algorithm, ensures high quality decompressed images and high compression rate. The results of the proposed algorithms are shown to be comparable in quality and performance with other existing methods.