This doctoral thesis consists of three essays. In the first essay I investigate the presence of productivity convergence in eight regional pulp and paper industries of U.S. and Canada over the period of 1971-2005. Expectation of productivity convergence in the pulp and paper industries of Canadian provinces and of the states of its southern neighbour is high since they are trading partners with fairly high level of exchanges in both pulp and paper products. Moreover, they share a common production technology that changed very little over the last century. I supplement the North-American regional data with national data for two Nordic countries, Finland and Sweden, which provides a scope to compare the productivity performances of four leading players in global pulp and paper industry. I find evidence in favour of the catch-up hypothesis among the regional pulp and paper industries of U.S. and Canada in my sample. The growth performance is at the advantage of Canadian provinces relative to their U.S. counterparts. However, it is not good enough to surpass the growth rates of this industry in the two Nordic countries. It is well-known that econometric productivity estimation using flexible functional forms often encounter violations of curvature conditions. However, the productivity literature does not provide any guidance on the selection of appropriate functional forms once they satisfy the theoretical regularity conditions. The second chapter of my thesis provides an empirical evidence that imposing local curvature conditions on the flexible functional forms affect total factor productivity (TFP) estimates in addition to the elasticity estimates. Moreover, I use this as a criterion for evaluating the performances of three widely used locally flexible cost functional forms - the translog (TL), the Generalized Leontief (GL), and the Normalized Quadratic (NQ) - in providing TFP estimates. Results suggest that the NQ model performs better than the other two functional forms in providing TFP estimates. The third essay capitalizes on newly available high frequency energy consumption data from commercial buildings in the District of Columbia (DC) to provide novel insights on the realized energy use impacts of energy efficiency standards in commercial buildings. Combining these data with hourly weather data and information on tenancy contract structure I evaluate the impacts of energy standards, contractual structure of utility bill payments, and energy star labeling on account level electricity consumption. Using this unique panel dataset, the analysis takes advantage of detailed building-level characteristics and the heterogeneity in the building age distribution, resulting in buildings constructed before and after mandatory energy standards came into effect. Estimation results suggest that in commercial buildings constructed under a code, electricity consumption is lower by about 0.48 kWh per cooling degree hour. When tenants pay for their own utilities, consumption is lower by 0.82 kWh per cooling degree hour. The Energy Star effect is a 0.31 kWh reduction per cooling degree hour. Finally, peak savings for all three variables of interest occur at 2pm in the summer months, whereas peak summer marginal prices at DC's local electric utility occur at 5pm.

The advancements in Artificial Intelligence (AI) have received large attention in recent years and increased awareness has led to massive societal benefits and new opportunities for industries able to capitalize on these emerging technologies. The pulp and paper industry is going through one of the most considerable transformations into Industry 4.0. Integrating AI technology in the manufacturing process of the pulp and paper industry has shown great potential, but there are uncertainties which direction companies are heading. This study is an investigation of the pulp and paper industry in collaboration with IBM that aims to fill a gap between academia and the progress companies are making. More specifically, this thesis is a multiple case study of the current state and barriers of AI technology in the Swedish pulp and paper industry, the future trends and expectations of AI and the way organizations are managing AI initiatives Semi-structured interviews were conducted with 11 participants from three perspectives and the data was thematically coded. Our analysis shows that the use of AI varies, and companies are primarily experimenting with a still immature technology. Several trends and areas with future potential were identified and it was shown that digital innovation management is highly regarded. We conclude that there are several barriers hindering further use of AI. However, continued progress with AI will provide large benefit long term in areas such as predictive maintenance and process optimization. Several measures taken to support initiatives with AI were identified and discussed. We encourage managers to take appropriate actions in the continued work toward AI integration and encourage further research in the area of potential reworks in R&D. / Framgångarna inom Artificiell Intelligens (AI) har fått stor uppmärksamhet de senaste åren och ökad medvetenhet har lett till stora fördelar för samhället liksom nya möjligheter för industrier som tar vara på dessa nya teknologier. Pappers- och massa industrin genomgår en av de mest omfattande transformationerna mot Industri 4.0. Integreringen av AI-teknologi i industrins tillverkningsprocesser has visat stor potential, men också osäkerhet kring vilken riktning företag är på väg mot. Denna studie är en undersökning av den svenska pappers- och massaindustrin, i samarbete med IBM, som syftar till att minska gapet mellan akademin och framstegen företag inom industrin tar. Mer specifikt är denna uppsats en kombinerad fallstudie av det nuvarande läget, barriärerna till AI-teknik i den svenska pappers- och massa industrin, de framtida trenderna och förväntningarna på AI och metoderna företag använder för att stötta AI-initiativ. Semi-strukturerade intervjuer genomfördes med 11 deltagare från tre olika perspektiv och datan var tematiskt kodad. Vår analys visar att användning av AI varierar och företag experimenterar huvudsakligen med omogen teknik. Flera trender och områden med potential för framtiden identifierades och det visades att digital innovationshantering är högt ansedd. Vi sammanfattar med att det finns flera barriärer som hindrar fortsatt användning av AI. Fortsatt arbete med AI-tekniken kommer leda till stora fördelar på lång sikt inom områden som prediktivt underhåll och fortsatt processoptimering. Flera åtgärder för att stötta AI-initiativ var identifierade och diskuterades. Vi uppmuntrar industrin att genomföra lämpliga åtgärder i det fortsatta arbetet mot AI-integration och uppmuntrar fortsatt forskning inom potentiella omstruktureringar inom FoU.