The roles of network embeddedness, market incentives, and slack resources in the adoption of clean technologies by firms in developing countries

ABSTRACT

Worldwide carbon dioxide emissions continue to increase driven by fossil fuel consumption and industrial discharges. Progress on carbon emission reduction requires firms to adopt clean technologies which minimize material and energy consumption. Technological change is particularly required in developing countries, where industrial emissions often lead to chronic urban pollution problems. In this study, we explore the antecedents of clean technology strategy by firms in developing countries. We combine the contingent natural resource-based view with the relational view to examine how network embeddedness, market incentives and slack resources influence adoption of clean technology. The empirical support for our hypotheses comes from data obtained from 342 firms that operated in the carbon-offset market during the years 2007 to 2009. We find that a firm’s relational network structure influences adoption of clean technologies, particularly when market incentives are low. Contrary to one of the hypotheses, the results of our paper suggest a negative relationship between a firm’s slack resources and its clean technology strategy. Our study highlights the benefits of networks in fostering adoption of clean technology in developing countries. Furthermore, we find that high market incentives (carbon price) decrease the probability of clean technology adoption, so adding to the view that firms respond to carbon-offset rules to realize high carbon revenues at the lowest cost.

Key policy insights

• High market incentives (carbon price) decrease the probability that firms in developing countries will adopt clean technology.

• This adds to concerns about the capability of the Clean Development Mechanism to deliver sustainable development.

• Even where market incentives are low, firms in developing countries are more likely to adopt clean technologies when they are embedded in a closed network of connected partners.

• To stimulate adoption of clean technology in developing countries, policy makers should focus on initiatives to facilitate partnerships between organizations operating in the carbon market and create opportunities for knowledge sharing and learning.

• By changing the policy focus to networks of organizations, the carbon market can bring about positive change in terms of shifting the firm behaviour.

KEYWORDS:

• Adoption of clean technologies
• carbon-offset market
• slack resources
• network embeddedness
• market incentives

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Naeem Ashraf http://orcid.org/0000-0002-2817-4235

Breeda Comyns http://orcid.org/0000-0002-6277-0698

Zeeshan Ahmed Bhatti http://orcid.org/0000-0003-0725-7039

Notes

1. The future of the CDM is uncertain after 31 December 2020, when the Kyoto Protocol’s second commitment period ends. Article 6 of the Paris Agreement introduces a new mechanism which aims to contribute to the mitigation of greenhouse gas emissions and to foster sustainable development. It is unclear whether the CDM will be discontinued after 2020, or whether it will remain operational in parallel to the Article 6 mechanism (UNFCCC, 2018).

2. CDM Project data can be accessed from Institute of Global Environmental Strategies from this address: https://pub.iges.or.jp/pub/iges-cdm-project-database.

3. Complete historical record and analysis of the CDM market can obtained from this address: http://www.cdmpipeline.org/.

4. Mintglobal (https://mintglobal.bvdinfo.com) is a proprietary database and contains financial information about the private and publicly listed firms.

5. KLD (Kinder, Lydenberg and Domini) is an independent ratings service which assesses firm performance across a range of social and environmental dimensions related to stakeholder concerns.

6. The dependent variable was a proportion, however, there were only 15 observations that lay between 0 and 1. We thus dropped these observations to avoid bias in the estimation.

7. The tests are available through overid and weakiv functions in STATA.