Expanding the use of wind energy for electricity generation forms an integral part of China’s efforts to address degraded air quality and climate change. However, the integration of wind energy into China’s coal-heavy electricity system presents significant challenges owing to wind’s variability and the grid’s system-wide inflexibilities. Here we develop a model to predict how much wind energy can be generated and integrated into China’s electricity mix, and estimate a potential production of 2.6 petawatt-hours (PWh) per year in 2030. Although this represents 26% of total projected electricity demand, it is only 10% of the total estimated physical potential of wind resources in the country. Increasing the operational flexibility of China’s coal fleet would allow wind to deliver nearly three-quarters of China’s target of producing 20% of primary energy from non-fossil sources by 2030.

China, the world’s largest energy consumer and greenhouse gas emitter, has made deploying wind-generated electricity a cornerstone of long-term plans to mitigate climate change, air pollution and other energy-related environmental impacts. Following rapid expansion in recent years, especially in remote, less populous areas, wind has faced significant challenges integrating into the coal-heavy power grid owing to its fundamental operational differences compared to conventional energy sources. We present the first assessment of China’s wind energy potential and its regional distribution that incorporates an operational model of the grid and undertakes systematic exploration of key uncertainties.

This thesis frames design in infrastructure public-private partnerships (P3s) as an exercise in negotiated collaboration. I investigate whether the collaborative design process in P3s can systematically deliver the benefits of innovation in design. The focus is on two aspects of the design process: project co-design, and collaboration mechanism. I find that both aspects enable innovation by driving project actors to learn about the design space and develop a shared understanding of the design problem. Learning through shared understanding not only improves quantifiable payoffs (Objective Value) but also enhances the actors’ psycho-social outcomes (Subjective Value).

Co-design is a process in which project actors simultaneously design technical and contractual features of a project. I developed a tradespace model to visualize and explore value trade-offs from co-design, using a desalination P3 as a project case. Co-design is a fundamental improvement over the traditional sequential design process because it reveals the zone of negotiated agreement, a frontier set of designs available to project actors, that can help them meet their own objectives while balancing value trade-offs. The combination of flexible modular designs and risk sharing revenue guarantee mechanisms emerged as a frontier design choice in the co-design analysis.

Communication and common knowledge are two different collaboration mechanisms that affect the design choices of project actors. A controlled design experiment with 112 experienced designers tested the relative effects of these two mechanisms. The role-playing designers negotiated design decisions for a desalination P3 using the co-design tradespace model. Only the communication mechanism systematically shifted outcomes. To increase the reliability of meeting uncertain water demand, the firm traded away an expected net present value profit share of 24% (p<0.001) on average, subject to the parameter assumptions. The water authority increased contractual payments by an expected net present value share of 6.6% (p<0.001) on average. Final designs in the exercise were on average 97.5% reliable in meeting uncertain water demand. Communication dominated common knowledge as a collaboration mechanism because it enabled participants to learn about the effects of modularity and revenue guarantees on counter-party outcomes and use these design features to negotiate value trade-offs.

Objective Value represents the technical (reliability) and economic (profits, payments) payoffs to project participants. Subjective Value on the other hand captures social psychological outcomes such as the degree of trust and rapport between collaborators and perceived fairness and legitimacy of the process, which are important for the partnering relationship. Participants in the 3 collaboration experiment overwhelmingly reported high Subjective Value scores, which are positively correlated with both their improved understanding of the project’s design objectives (r = 0.37, ρ = 0.41, p<0.001) and their ability to communicate with collaborators to agree on design choices (r = 0.36, ρ = 0.36, p = 0.001).

This work directly addresses the literature on infrastructure public-private partnerships and shows how negotiated collaboration can create objective as well as psychosocial benefits for a stronger partnering relationship. The co-design approach speaks to the literature on systems design to emphasize how a systems view can help designers balance trade-offs. The experimental study is a methodological contribution to both the design and negotiations literature, applying the Subjective Value framework in an integrated design setting.

Mexico’s climate policy sets ambitious national greenhouse gas (GHG) emission reduction targets—30% versus a business-as-usual baseline by 2020, 50% versus 2000 by 2050. However, these goals are at odds with recent energy and emission trends in the country. Both energy use and GHG emissions in Mexico have grown substantially over the last two decades. We investigate how Mexico might reverse current trends and reach its mitigation targets by exploring results from energy system and economic models involved in the CLIMACAP-LAMP project. To meet Mexico’s emission reduction targets, all modeling groups agree that decarbonization of electricity is needed, along with changes in the transport sector, either to more efficient vehicles or a combination of more efficient vehicles and lower carbon fuels. These measures reduce GHG emissions as well as emissions of other air pollutants. The models find different energy supply pathways, with some solutions based on renewable energy and others relying on biomass or fossil fuels with carbon capture and storage. The economy-wide costs of deep mitigation could range from 2% to 4% of GDP in 2030, and from 7% to 15% of GDP in 2050. Our results suggest that Mexico has some flexibility in designing deep mitigation strategies, and that technological options could allow Mexico to achieve its emission reduction targets, albeit at a cost to the country.

© 2016 Elsevier

The sustainability of future water resources is of paramount importance and is affected by many factors, including population, wealth and climate. Inherent in current methods to estimate these factors in the future is the uncertainty of their prediction. In this study, we integrate a large ensemble of scenarios—internally consistent across economics, emissions, climate, and population—to develop a risk portfolio of water stress over a large portion of Asia that includes China, India, and Mainland Southeast Asia in a future with unconstrained emissions. We isolate the effects of socioeconomic growth from the effects of climate change in order to identify the primary drivers of stress on water resources. We find that water needs related to socioeconomic changes, which are currently small, are likely to increase considerably in the future, often overshadowing the effect of climate change on levels of water stress. As a result, there is a high risk of severe water stress in densely populated watersheds by 2050, compared to recent history. There is strong evidence to suggest that, in the absence of autonomous adaptation or societal response, a much larger portion of the region’s population will live in water-stressed regions in the near future. Tools and studies such as these can effectively investigate large-scale system sensitivities and can be useful in engaging and informing decision makers.

© 2016 the authors