No way out? The double-bind in seeking global prosperity alongside mitigated climate change

In a prior study, I introduced a simple economic growth model designed to be consistent with general thermodynamic laws. Unlike traditional economic models, civilization is viewed only as a well-mixed global whole with no distinction made between individual nations, economic sectors, labor, or capital investments. At the model core is an observationally supported hypothesis that the global economy's current rate of primary energy consumption is tied through a constant to a very general representation of its historically accumulated wealth. Here, this growth model is coupled to a linear formulation for the evolution of globally well-mixed atmospheric CO2 concentrations. While very simple, the coupled model provides faithful multi-decadal hindcasts of trajectories in gross world product (GWP) and CO2. Extending the model to the future, the model suggests that the well-known IPCC SRES scenarios substantially underestimate how much CO2 levels will rise for a given level of future economic prosperity. For one, global CO2 emission rates cannot be decoupled from wealth through efficiency gains. For another, like a long-term natural disaster, future greenhouse warming can be expected to act as an inflationary drag on the real growth of global wealth. For atmospheric CO2 concentrations to remain below a "dangerous" level of 450 ppmv, model forecasts suggest that there will have to be some combination of an unrealistically rapid rate of energy decarbonization and nearly immediate reductions in global civilization wealth. Effectively, it appears that civilization may be in a double-bind. If civilization does not collapse quickly this century, then CO2 levels will likely end up exceeding 1000 ppmv; but, if CO2 levels rise by this much, then the risk is that civilization will gradually tend towards collapse

An initial matching and mapping for dense 3D object tracking in augmented reality applications

Augmented Reality (AR) applications rely on efficient and robust methods of tracking. One type of tracking uses dense 3D point data representations of the object to track. As opposed to sparse, dense tracking approaches are highly accurate and precise by considering all of the available data from a camera. A major challenge to dense tracking is that it requires a rough initial matching and mapping to begin. A matching means that from a known object, we can determine the object exists in the scene, and a mapping means that we can identify the position and orientation of an object with respect to the camera. Current methods to provide the initial matching and mapping require the user to manually input parameters, or wait an extended amount of time for a brute force automatic approach. The research presented in this thesis develops an automatic initial matching and mapping for dense tracking for AR, facilitating natural AR systems that track 3D objects. To do this, an existing offline method for registration of ideal 3D object point sets is proposed as a starting point. The method is improved and optimized in four steps to address the requirements and challenges for dense tracking in AR with a noisy consumer sensor. A series of experiments verifies the suitability of the optimizations, using increasingly large and more complex scene point clouds, and the results are presented

Gains in economic energy efficiency as the impetus for increasing atmospheric carbon dioxide

Journal ArticleGrowth of anthropogenic carbon dioxide (CO2) emissions is frequently diagnosed as a product of population, per capita economic production, the energy intensity of economic production (or inverse of its energy efficiency), and the carbon intensity of energy. This paper introduces an alternative, prognostic emissions model that accounts for human system feedbacks: economic production adds to a generalized form of infrastructure; infrastructure enables energy consumption through a constant of proportionality; in return, energy consumption powers economic production: CO2 is emitted as the waste-product. Core assumptions in the model are shown to be supported by economic records from recent decades, implying that, perhaps surprisingly, it is the growing energy efficiency of the economy, not increasing population or standard of living, that most directly explains accelerating CO2 emissions. Thus, further increases in energy efficiency are likely to backfire as a mitigation strategy. Instead, any strategy for limiting future atmospheric CO2 emissions requires strong and accelerating reductions in the carbon content of energ

Novel basis for interpreting recent acceleration of anthropogenic carbon dioxide emissions

Journal ArticleThis paper presents a simple thermodynamic model for understanding economic and carbon dioxide emissions growth

Using Citizen Science Data to Predict the Probability of Occupancy, Colonization, and Extinction of Bicknell’s Thrush (Catharus bicknelli) in the White Mountains of New Hampshire

Bicknell’s Thrush (Catharus bicknelli) is a neotropical migrant bird species that breeds in the montane spruce (Picea spp.) and balsam fir (Abies balsamea) forests of Maine, New Hampshire, Vermont, New York, and eastern Canada. This species has a restricted range, a decreasing population, and is facing the significant threat of climate change. I used a multi-season occupancy model to predict the probability of occupancy, colonization, and extinction, of Bicknell’s Thrush, while accounting for the probability of detection. From 2010 to 2021, 597 detections of Bicknell’s Thrush from 276 sampling stations in New Hampshire were recorded. Covariates investigated were elevation, proportion of evergreen forest, deciduous forest, and other landcover types. Detection covariates included survey date, point count start time, and year. The model predicted an overall negative relationship between detection probability and point count start time, where detection declined after 5:00am and then increased after 7:30am. The probabilities of site occupancy and site colonization were best represented by the additive and interactive relationships of elevation and proportion of evergreen forest, respectively. Elevation was the covariate that had the largest beta estimate for occupancy and colonization probability. Probability of local site extinction is best represented by proportion of evergreen forest and has a slight negative relationship. As the White Mountain National Forest of New Hampshire continues to warm, the relationship between a changing climate, upslope movement of the montane spruce-fir forest, and species occupancy will be increasingly important for conservation of the country’s largest concentration of Bicknell’s Thrush habitat