Being able to replicate research results is the hallmark of science. Replication of research findings using computational models should, in principle, be possible. In this manuscript, we assess code sharing and model documentation practices of 7500 publications about individual-based and agent-based models. The code availability increased over the years, up to 18% in 2018. Model documentation does not include all the elements that could improve the transparency of the models, such as mathematical equations, flow charts, and pseudocode. We find that articles with equations and flow charts being cited more among other model papers, probably because the model documentation is more transparent. The practices of code sharing improve slowly over time, partly due to the emergence of more public repositories and archives, and code availability requirements by journals and sponsors. However, a significant change in norms and habits need to happen before computational modeling becomes a reproducible science.

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This study argues that scholars and policy-makers need to understand environmental activists better to bridge the gap between growing activism and policy. Conventional wisdom is that environmental activists generally support stronger climate policies. But there is still little understanding about diversity of views within activist groups when it comes to specific policies, and existing studies indicate that their views are not uniform, which can weaken their impact as a group. Activists might unite to demand change, but not necessarily agree on details of the desired change. Exploring the differences within the group, this paper focuses on how to nudge those who already share favorable attitudes towards policies that mitigate climate change. The motivation has been to see, in presence of general support for stronger environmental policies, whether this support could be channeled into more specific policies. We first take on a methodological challenge to construct an index of environmental predisposition. Then drawing from existing social-behavioral scholarship, we analyze results of an experimental survey with select treatments previously reported as promising. In November and December 2019, we collected responses from 119 participants at the Fridays for Future demonstrations in Germany. The results indicate that there are indeed important differences within the group, and nudging effects exist even in this rather strongly predisposed group, with participants assigned to the experimental group showing higher levels of support for the introduction of a carbon tax that is traditionally seen as a difficult policy to gain widespread public support. We find that those who score neither too high nor too low are more likely to respond to nudging. Yet, the effects vary for general outcomes such as policy support, behavioral intentions, and environmental citizenship. Overall, the findings show the value of understanding the heterogeneity of individual views within environmental movements better and directing interventions in large resource systems such as climate to specific issues and target groups for accelerating transformations towards sustainability.

Due to a production error, a portion of text was omitted from the beginning of the fourth and final paragraph of section 3.1. Currently, the paragraph begins as follows:'(n = 25) had more than six trips by air in the last three years, including 7.56% (n = 9) who had more than ten trips.' However, the following is the correct beginning to the paragraph, with the omitted portion highlighted in bold.Third-reported environmental footprint. Conservatively,10%-20% of respondents reported relatively high environmental footprint and 10%-20% indicated very low environmental footprint. From the surveyed respondents, 21.01% (n = 25) had more than six trips by air in the last three years, including 7.56% (n = 9) who had more than ten trips.

Will you be able to run your computational models in the future? Even with well-documented code, this can be difficult due to changes in the software frameworks and operating systems that your code was built on. In this paper we discuss the use of containers to preserve code and their software dependencies to reproduce simulation results in the future. Containers are standalone lightweight packages of the original model software and their dependencies that can be run independent of the platform. As such they are suitable for reuse and sharing results. However, the use of containers is rare in the field of modeling social-environmental systems. We provide an introduction to the basic principles of containerization, argue why it would be beneficial if this tool became common practice in the field, describe a conceptual walkthrough to the process of containerizing a model, and reflect on near future directions of containerization workflows.