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Bioeconomic models are analytical tools that integrate biophysical and economic models. These models allow for analysis of the biological and economic changes caused by human activities. The biophysical and economic components of these models are developed based on historical observations or theoretical relations. Technically these models may have various levels of complexity in terms of equation systems considered in the model, modeling activities, and programming languages. Often, biophysical components of the models include crop or hydrological models. The core economic components of these models are optimization or simulation models established according to neoclassical economic theories. The models are often developed at farm, country, and global scales, and are used in various fields, including agriculture, fisheries, forestry, and environmental sectors. Bioeconomic models are commonly used in research on environmental externalities associated with policy reforms and technological modernization, including climate change impact analysis, and also explore the negative consequences of global warming. A large number of studies and reports on bioeconomic models exist, yet there is a lack of studies describing the multiple uses of these models across different disciplines.

The development of information infrastructures that make ecological research data available has increased in recent years, contributing to fundamental changes in ecological research. Science and Technology Studies (STS) and the subfield of Infrastructure Studies, which aims at informing infrastructures’ design, use, and maintenance from a social science point of view, provide conceptual tools for understanding data infrastructures in ecology. This perspective moves away from the language of engineering, with its discourse on physical structures and systems, to use a lexicon more “social” than “technical” to understand data infrastructures in their informational, sociological, and historical dimensions. It takes a holistic approach that addresses not only the needs of ecological research but also the diversity and dynamics of data, data work, and data management. STS research, having focused for some time on studying scientific practices, digital devices, and information systems, is expanding to investigate new kinds of data infrastructures and their interdependencies across the data landscape. In ecology, data sharing and data infrastructures create new responsibilities that require scientists to engage in opportunities to plan, experiment, learn, and reshape data arrangements. STS and Infrastructure Studies scholars are suggesting that ecologists as well as data specialists and social scientists would benefit from active partnerships to ensure the growth of data infrastructures that effectively support scientific investigative processes in the digital era.

Megacities (metropolitan areas with populations over 10 million) and large urban centers present a major challenge for the global environment. Transportation, industrial activities, and energy demand have increased in megacities due to population growth and unsustainable urban development, leading to increasing levels of air pollution that subject the residents to the health risks associated with harmful pollutants, and impose heavy economic and social costs. Although much progress has been made in reducing air pollution in developed and some developing world megacities, there are many remaining challenges in achieving cleaner and breathable air for their residents. As centers of economic growth, scientific advancement, and technology innovation, however, these urban settings also offer unique opportunities to capitalize on the multiple benefits that can be achieved by optimizing energy use, reducing atmospheric pollution, minimizing greenhouse gas emissions, and bringing many social benefits. Realizing such benefits will, however, require strong and wide-ranging institutional cooperation, public awareness, and multi-stakeholder involvement. This is especially critical as the phenomenon of urbanization continues in virtually all countries of the world, and more megacities will be added to the world, with the majority of them located in developing countries. The air quality and emission mitigation strategies of eight megacities—Mexico City, Beijing, Shanghai, Shenzhen, Chengdu, Delhi, Kolkata, and Mumbai—are presented as examples of the environmental challenges experienced by large urban centers. While these megacities share common problems of air pollution due to the rapid growth in population and urbanization, each city has its own unique circumstances—geographical location, meteorology, sources of emissions, human and financial resources, and institutional capacity—to address them. Nevertheless, the need for an integrated multidisciplinary approach to air quality management is the same. Mexico City’s air pollution problem was considered among the worst in the world in the 1980s due to rapid population growth, uncontrolled urban development, and energy consumption. After three decades of implementing successive comprehensive air quality management programs that combined regulatory actions with technological change and were based on scientific, technical, social, and political considerations, Mexico City has made significant progress in improving its air quality; however, ozone and particulate matter are still at levels above the respective Mexican air quality standards. Beijing, Shanghai, Shenzhen, and Chengdu are microcosms of megacities in the People’s Republic of China, with rapid socioeconomic development, expanding urbanization, and swift industrialization since the era of reform and opening up began in the late 1970s, leading to severe air pollution. In 2013, the Chinese government issued the Action Plan for Air Pollution Prevention and Control. Through scientific research and regional coordinated air pollution control actions implemented by the Chinese government authority, the concentration of atmospheric pollutants in several major cities has decreased substantially. About 20% of total megacities’ populations in the world reside in Indian megacities; the population is projected to increase, with Delhi becoming the largest megacity by 2030. The increased demands of energy and transportation, as well as other sources such as biomass burning, have led to severe air pollution. The air quality trends for some pollutants have reduced as a result of emissions control measures implemented by the Indian government; however, the level of particulate matter is still higher than the national standards and is one of the leading causes of premature deaths. The examples of the eight cities illustrate that although most air pollution problems are caused by local or regional sources of emissions, air pollutants are transported from state to state and across international borders; therefore, international coordination and collaboration should be strongly encouraged. Based on the available technical-scientific information, the regulations, standards, and policies for the reduction of polluting emissions can be formulated and implemented, which combined with adequate surveillance, enforcement, and compliance, would lead to progressive air quality improvement that benefits the population and the environment. The experience and the lessons learned from the eight megacities can be valuable for other large urban centers confronting similar air pollution challenges.