Urban agglomeration, an established urban spatial pattern, contributes to the spatial association and dependence of city-level CO₂ emission distribution while boosting regional economic growth. Exploring this spatial association and dependence is conducive to the implementation of effective and coordinated policies for regional level CO₂ reduction. This study calculated CO₂ emissions from 2005–2016 in the Chengdu-Chongqing urban agglomeration with the IPAT model, and empirically explored the spatial structure pattern and association effect of CO₂ across the area leveraged by the social network analysis. The findings revealed the following: (1) The spatial structure of CO₂ emission in the area is a complex network pattern, and in the sample period, the CO₂ emission association relations increased steadily and the network stabilization remains strengthened; (2) the centrality of the cities in this area can be categorized into three classes: Chengdu and Chongqing are defined as the first class, the second class covers Deyang, Mianyang, Yibin, and Nanchong, and the third class includes Zigong, Suining, Meishan, and Guangan—the number of cities in this class is on the rise; (3) the network is divided into four subgroups: the area around Chengdu, south Sichuan, northeast Sichuan, and west Chongqing where the spillover effect of CO₂ is greatest; and (4) the higher density of the global network of CO₂ emission considerably reduces regional emission intensity and narrows the differences among regions. Individual networks with higher centrality are also found to have lower emission intensity.

Wave height and wave period are important oceanic environmental factors that are used to describe the randomness of a wave. Within the field of ocean engineering, the calculation of design wave height is of great significance. In this paper, a periodic maximum entropy distribution function with four undetermined parameters is derived by means of coordinate transformation and solving conditional variational problems. A double entropy joint distribution function of wave height and wave period is also derived. The function is derived from the maximum entropy wave height function and the maximum entropy periodic function, with the help of structures of the Copula function. The double entropy joint distribution function of wave height and wave period is not limited by weak nonlinearity, nor by normal stochastic process and narrow spectrum. Besides, it can fit the observed data more carefully and be more widely applicable to nonlinear waves in various cases, owing to the many undetermined parameters it contains. The engineering cases show that the recurrence level derived from the double entropy joint distribution function is higher than that from the extreme value distribution using the single variables of wave height or wave period. It is also higher than that from the traditional joint distribution function of wave height and wave period.

As the largest consumer of petroleum and second highest producer of greenhouse gas emissions, the United States currently is a leading country in bioenergy production driven by a series of policies. One such public program that directly subsidizes biomass feedstock growers is the Biomass Crop Assistance Program (BCAP), which recently received attention for stimulating the development of bioenergy. However, critiques were also raised, concerning the possibility of intensifying the feedstock competition between bioenergy and forest products. This study, therefore, aimed at assessing the effects of BCAP on the forest product markets with the Global Forest Products Model (GFPM). Three alternative scenarios were designed to simulate three payments in BCAP. In the first scenario, the matching payment was simulated by adjusting the manufacturing cost of fuelwood and particleboard. In the second scenario, establishment payment was simulated by adjusting the supply rate of industrial roundwood. In the third scenario, the annual payment was simulated by linking the supply change rate with the soil rental rate for industrial roundwood. We found that, under the matching payment scenario, industrial roundwood and particleboard will experience a sharp decrease in production and an increase in price. For establishment payments, industrial roundwood will experience a continuous increase in production and a decrease in price. For annual payments, the industrial roundwood will experience a V-shape pattern production.

This paper investigates the spatial heterogeneity of landowners’ willingness to supply three bioenergy crops: switchgrass, Miscanthus, and willow, in the northeastern United States. Spatial heterogeneity might arise for several reasons. For example, landowners closer to bioenergy processing plants might be more likely to be willing to supply bioenergy crops, and landowners who are more willing to supply bioenergy crops may be spatially clustered because they share similar land attributes, demographics, experiences, and/or values. Using high‐resolution GIS data related to the location of pellet plants utilizing bioenergy crops and survey data related to landowners’ characteristics including spatial location, we estimate a spatial probit model to explain the variation in individual‐specific reservation prices (RPs)—the feedstock price at which landowners become willing to supply a bioenergy crop. We find that respondents’ RP is lower the closer they live to their nearest pellet plant and spatial dependency is only present for switchgrass supply. We also identify three economic hotspots (areas with high potential supply and low RPs) for each bioenergy crop. We believe that bioenergy supply chains could be developed around these hotspots.