These days as never before, people are much more conscious of the climate change and environmental sustainability (World development report, 2015). The constantly increasing green concerns in consumer markets as well as rapidly growing pressure from governmental regulations are now driving companies to manage their daily activities from an ecological perspective (Mutingi et al., 2014). Besides the cost, lead-time and quality, firms today need to consider the improvement of green performance as a fundamental competitive priority when doing businesses (Bloom and Morton, 1991; Azzone and Bertele, 1994). In earlier years, firms focused on the internal green measurements such as pollution control to alleviate the environmental influence of their production. Recently, external practices (e.g., green purchasing and eco-design) have started to be widely implemented because they come to realize that the environmental crisis in other firms can also bring harm to them via disruptions along the supply chain (Corbett and Klassen, 2000). Therefore, green supply chain management (GSCM) has then emerged as an important topic in both academia and practice, which requires firms to integrate environmental thinking into the whole supply chain. Throughout the past decade, researchers have conducted a large number of relative investigations, and one of the main streams is to empirically examine the performance of GSCM, which is supposed to provide companies with constructive guidance for the adoption of specific practices (e.g., Zhu and Sarkis, 2004; Chavez et al., 2015; Govindan et al., 2015). However, the findings of prior empirical studies do not always correspond with each other, which may make practitioners confused when they intend to initiate GSCM and also prevent the further advancement of GSCM study. For instance, Rao and Holt (2005) indicated that firms adopting GSCM in Southeast Asia witness evident increases in both competitiveness and economic performance. But contrarily, Zhu et al. (2007) argued that little significant improvement in economic performance is found within firms adopting GSCM in China. Hence, there should now be a strong motivation for us to make a more comprehensive quantitative analysis of the prolific GSCM literature, which is able to shed light on those inconsistencies in prior empirical results by looking into potential moderators that might influence them. Endeavors to consolidate previous empirical results of GSCM studies have also been made throughout these years. But most of them are either qualitative (e.g., Jung, 2011; Chen et al., 2013) or only based on small samples (e.g., Rao and Holt, 2005). Few of them have attempted to integrate the prior researches from a quantitative perspective. On the contrary, meta-analysis is a statistical technique designed to quantitatively synthesize research findings across a large number of studies, which has been used as an effective analysis tool in medical and clinical areas for over two decades (e.g., Lau and Chalmers, 1995; LeLorier et al., 1997; Borenstein et al., 2009; Bowater et al., 2015), and has also recently proved its efficiency in management field (e.g., Geyskens et al., 2009; Melo et al., 2009; Lu et al., 2015). The widespread use of meta-analysis attests to its growing reputation as a tool for consolidating prior knowledge and explaining mixed findings. Therefore, in our study, we abandon the traditional narrative and vote-counting methodologies, and then turn to meta-analysis, which can help us to address the two following research questions.