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On November 29, 2019, 12 days before its announcement, information on the ambitions of the European Green Deal was leaked. The leakage should have triggered a Europe-wide systemic shock to financial markets without an accompanying announcement of supportive measures. Applying event study methodology to a sample of 600 European large and mid-cap stocks, we find that the overall market reaction was indeed significantly negative, albeit moderate. Abnormal returns gradually decline with increasing greenhouse gas emissions levels. Conversely, the official announcement emphasizing financial support and the green growth narrative did not ignite a positive market reaction. The results are largely robust in multivariate regressions. We conclude that market participants incorporate available emissions information into (short-term) reassessments after a significant change in environmental policy becomes known.

Even with a normalized and standardized biofuel shock, the wide range of land-use change estimates and their associated greenhouse gas (GHG) emissions have raised concern on the adequacy of existing agricultural models in this new area of analysis. In particular, reducing bias and improving precision of impact estimates are of primary concern to policy makers. This paper provides a detailed overview of the FAPRI-CARD agricultural modeling system, with particular emphasis on the modifications recently introduced to reduce bias in the results. We illustrate the impact of these new model features using the example of the new yield specification that now includes updated trend parameter, intensification and extensification effects, and a spatially disaggregated Brazil specification. The paper also provides a taxonomy of the many types of uncertainty surrounding any analysis, including parameter-coefficient uncertainty and exogenous variable uncertainty, identifying where specific types of uncertainty originate, and how they interact. Finally, FAPRI-CARD's long experience in using stochastic analysis is presented as a viable approach in addressing uncertainty in the analysis of changes in the agricultural sector, associated land-use change, and impacts on GHG emissions.

To achieve the Paris Agreement’s goals, many cities are building satellite cities to relieve the population and environment pressure of the central city. However, past experiences showed that long-term effects of such a solution were partially limited, due to limited attention on the effects of energy consumption and carbon emissions, depending on the dynamics of population and industrial development. This paper overcomes the previous limitations, applying a Long-range Energy Alternatives Planning model to the area of Xiong’an New District, an area extending from Hebei province (China) and nearby Beijing, is planned to support the development of Beijing. The proposed model was based on three different population migration, industrial and transposition scenarios to test their impacts on urban greenhouse gas (GHG) emissions. Results show that: (1) Increased population and building area will markedly increase GHG emissions from residential consumption in Hebei province, while slightly decrease GHG emissions in Beijing. (2) Green planning, including industrial structure changes, industrial transformation, will markedly decrease the GHG emissions in Hebei provinces and it can take down for the emissions increase due to the population migration. This paper proved the effectiveness of a multi-scalar, multi-dimensional, and multi-actor modeling approach for a satellite city and new town development planning, implying that a similar approach could be applied in planning and managing the development of future satellite cities.

China is the world’s third-largest beef producer after the United States and Brazil. Despite this, China is not complying with the environmental measures that have been adopted elsewhere by the beef industry. Current Chinese regulations and standards covering the negative impact of the meat industry are scarce; research studies measuring the impact of current policies on air pollution have a focus other than the beef industry, such as thermal power, metallurgy, and iron and steel production. The case discusses the current situation of meat production in China and its effect on the environment.

As the low-carbon pilot area, Hubei Province has faced tremendous pressure on GHG emissions reduction with rapid economic growth. However, Guangdong Province has balanced the economic development and energy consumption well. “Guangdong Mode” represents the reasonable industry structure, energy structure and lower energy intensity. In this paper, LEAP-Hubei model was established to predict the level of GHG emissions and energy consumption in Hubei from now to 2025. Three scenarios were set: Base Scenario, “Guangdong Mode” I and “Guangdong Mode” II. The results show that “Guangdong Mode” and The “New Normal” have a significant effect on energy consumption and GHG emissions reduction for Hubei. When “Guangdong Mode” and the “New Normal” meet in Hubei, the energy consumption and GHG emissions will reach the top.