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While the Global North is historically responsible for the majority of greenhouse gas emissions in the atmosphere, Newly Industrializing Countries (NICs) are expected to overtake developed country emissions in the coming years. At the same time, NICs are climbing the ladder of the global economy, increasing their competitiveness on the global stage and catching up with technological competencies of developed economies. Against this background, this paper explores innovation and collaboration in Climate Change Mitigation Technologies (CCMTs) in NICs. The research question is whether the propensity to innovate and diffuse CCMTs is impacted by technological collaboration with two highly developed countries, Germany and The United States. The sample of NICs includes the BRICS (Brazil, Russia, India, China, South Africa) plus Israel, Mexico, and Turkey, in a panel from 1995 to 2015. The empirical results suggest that collaboration with both Germany and the U.S. is highly significant for domestic CCMT innovation in NICs. These findings are important because, stepping beyond the literature on the merits and drawbacks of global climate governance tools such as the Clean Development Mechanism (CDM) and related UNFCCC processes, they show that collaboration for climate and environmental technologies could become a key tool to significantly improve the chances to stay in line with the Paris Climate Agreement. Finally, the policy advice for NICs and developing countries is to, above all else, focus on incubating strong technological innovation systems, including strengthening domestic Intellectual Property Rights (IPR), as well as to enhance technological collaboration with developed countries. • We present a time-series panel from 1995 to 2015. • This research explores Climate Mitigation Technologies (CCMTs) in Newly Industrializing Countries. • Technological collaboration with Germany and the U.S. drives NICs CCMT innovation. • Novel empirical models capture the role of absorptive capacities and domestic innovation systems. • A machine-learning governance index is used as a reliable proxy for governance quality. [ABSTRACT FROM AUTHOR]