Measuring CO2 emissions induced by online and brick-and-mortar retailing

We develop a method for empirically measuring the difference in carbon footprint between traditional and online retailing (“e-tailing”) from entry point to a geographical area to consumer residence. The method only requires data on the locations of brick-and-mortar stores, online delivery points, and residences of the region’s population, and on the goods transportation networks in the studied region. Such data are readily available in most countries, so the method is not country or region specific. The method has been evaluated using data from the Dalecarlia region in Sweden, and is shown to be robust to all assumptions made. In our empirical example, the results indicate that the average distance from consumer residence to a brick-and-mortar retailer is 48.54 km in the studied region, while the average distance to an online delivery point is 6.7 km. The results also indicate that e-tailing increases the average distance traveled from the regional entry point to the delivery point from 47.15 km for a brick-and-mortar store to 122.75 km for the online delivery points. However, as professional carriers transport the products in bulk to stores or online delivery points, which is more efficient than consumers’ transporting the products to their residences, the results indicate that consumers switching from traditional to e-tailing on average reduce their CO2 footprints by 84% when buying standard consumer electronics products. 

Measuring CO2 emissions induced by online and brick-and-mortar retailing

We develop a method for empirically measuring the difference in carbon footprint between traditional and online retailing (“e-tailing”) from entry point to a geographical area to consumer residence. The method only requires data on the locations of brick-and-mortar stores, online delivery points, and residences of the region’s population, and on the goods transportation networks in the studied region. Such data are readily available in most countries, so the method is not country or region specific. The method has been evaluated using data from the Dalecarlia region in Sweden, and is shown to be robust to all assumptions made. In our empirical example, the results indicate that the average distance from consumer residence to a brick-and-mortar retailer is 48.54 km in the studied region, while the average distance to an online delivery point is 6.7 km. The results also indicate that e-tailing increases the average distance traveled from the regional entry point to the delivery point from 47.15 km for a brick-and-mortar store to 122.75 km for the online delivery points. However, as professional carriers transport the products in bulk to stores or online delivery points, which is more efficient than consumers’ transporting the products to their residences, the results indicate that consumers switching from traditional to e-tailing on average reduce their CO2 footprints by 84% when buying standard consumer electronics products. 

How does the use of different road networks effect the optimal location of facilities in rural areas?

The p-median problem is often used to locate P service facilities in a geographically distributed population. Important for the performance of such a model is the distance measure. Distance measure can vary if the accuracy of the road network varies. The rst aim in this study is to analyze how the optimal location solutions vary, using the p-median model, when the road network is alternated. It is hard to nd an exact optimal solution for p-median problems. Therefore, in this study two heuristic solutions are applied, simulating annealing and a classic heuristic. The secondary aim is to compare the optimal location solutions using dierent algorithms for large p-median problem. The investigation is conducted by the means of a case study in a rural region with an asymmetrically distributed population, Dalecarlia. The study shows that the use of more accurate road networks gives better solutions for optimal location, regardless what algorithm that is used and regardless how many service facilities that is optimized for. It is also shown that the simulated annealing algorithm not just is much faster than the classic heuristic used here, but also in most cases gives better location solutions.

How does data quality in a network affect heuristic solutions?

To have good data quality with high complexity is often seen to be important. Intuition says that the higher accuracy and complexity the data have the better the analytic solutions becomes if it is possible to handle the increasing computing time. However, for most of the practical computational problems, high complexity data means that computational times become too long or that heuristics used to solve the problem have difficulties to reach good solutions. This is even further stressed when the size of the combinatorial problem increases. Consequently, we often need a simplified data to deal with complex combinatorial problems. In this study we stress the question of how the complexity and accuracy in a network affect the quality of the heuristic solutions for different sizes of the combinatorial problem. We evaluate this question by applying the commonly used p-median model, which is used to find optimal locations in a network of p supply points that serve n demand points. To evaluate this, we vary both the accuracy (the number of nodes) of the network and the size of the combinatorial problem (p). The investigation is conducted by the means of a case study in a region in Sweden with an asymmetrically distributed population (15,000 weighted demand points), Dalecarlia. To locate 5 to 50 supply points we use the national transport administrations official road network (NVDB). The road network consists of 1.5 million nodes. To find the optimal location we start with 500 candidate nodes in the network and increase the number of candidate nodes in steps up to 67,000 (which is aggregated from the 1.5 million nodes). To find the optimal solution we use a simulated annealing algorithm with adaptive tuning of the temperature. The results show that there is a limited improvement in the optimal solutions when the accuracy in the road network increase and the combinatorial problem (low p) is simple. When the combinatorial problem is complex (large p) the improvements of increasing the accuracy in the road network are much larger. The results also show that choice of the best accuracy of the network depends on the complexity of the combinatorial (varying p) problem.

Predicting entry of Swedish wholesale firms into local markets

Applying microeconomic theory, we develop a forecasting model for firm entry into local markets and test this model using data from the Swedish wholesale industry. The empirical analysis is based on directly estimating the profit function of wholesale firms. As in previous entry studies, profits are assumed to depend on firm- and location-specific factors,and the profit equation is estimated using panel data econometric techniques. Using the residuals from the profit equation estimations, we identify local markets in Sweden where firm profits are abnormally high given the level of all independent variables included in the profit function. From microeconomic theory, we then know that these local markets should have higher net entry than other markets, all else being equal, and we investigate this in a second step,also using a panel data econometric model. The results of estimating the net-entry equation indicate that four of five estimated models have more net entry in high-return municipalities, but the estimated parameter is only statistically significant at conventional levels in one of our estimated models.

Implementation of Video Monitoring In Aluminium Industry

The aim was to evaluate results and experiences from development of new technology, a training program and implementation of strategies for the use of a video exposure monitoring method, PIMEX. Starting point of this study is an increased incidence of asthma among workers in the aluminium industry. Exposure peaks of fumes are supposed to play an important role. PIMEX makes it possible to link used work practice, use of control technology, and so forth to peaks. Nine companies participated in the project, which was divided into three parts, development of PIMEX technology, production of training material, and training in use of equipment and related strategies. The use of the video exposure monitoring method PIMEX offers prerequisites supporting workers participation in safety activities. The experiences from the project reveal the importance of good timing of primary training, technology development, technical support, and follow up training. In spite of a delay of delivery of the new technology, representatives from the participating companies declared that the experiences showed that PIMEX gave an important contribution for effective control of hazards in the companies. Eight out of nine smelters used the PIMEX method as a part of a strategy for control of workers exposure to fumes in potrooms. Possibilities to conduct effective control measures were identified. This article describes experiences from implementation of a, for this branch, new method supporting workers participation for workplace improvements.

Speed prediction for triggering vehicle activated signs

Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the optimal trigger speed for such signs. Adaptive neuro fuzzy (ANFIS), classification and regression tree (CART) and random forest (RF) were developed to predict one step ahead speed during all times of the day. The developed models were evaluated and compared to the results obtained from artificial neural network (ANN), multiple linear regression (MLR) and naïve prediction using traffic speed data collected at four sites located in Sweden. The data were aggregated into two periods, a short term period (5-min) and a long term period (1-hour). The results of this study showed that using RF is a promising method for predicting mean speed in the two proposed periods.. It is concluded that in terms of performance and computational complexity, a simplistic input features to the predicitive model gave a marked increase in the response time of the model whilse still delivering a low prediction error.

Defining the links between retail price strategies and price tactics

Retailers tend to become trapped in a price-promotion war where price issues are dealt with on a short-term basis, indicating almost solely tactical choices. Since price is the only part of the marketing mix providing direct revenues to the organisation, it should also be of strategic importance for the retailer. Not only in practice are price tactics often separated from pricing strategies, it is also the case in research where these are often studied in isolation from each other probably due to their individual complexity. This paper contributes to both the research area and practice by discussing these two complex areas together, and the essence of both strategy and tactics are defined. By considering the planning horizon for the retailer this paper further contributes by defining the links between price strategy and price tactic. The conclusion shows the importance of clearly establishing which analytical level is being analysed.

Regionalekonomisk betydelse av travsporten : exemplet Dalatravet Rättvik

Tidigare studier har visat att hästnäringen inklusive spridningseffekter årligen omsätter ca 46 miljarder kronor och bidrar till sysselsättning för ca 30 000 helårsverken. Motsvarande beräkningar för travsportens del av hästnäringen eller för en mindre region finns däremot inte. Denna rapport syftar till att uppskatta regionalekonomisk betydelse av travsport i Sverige med utgångspunkt i en mindre travbanas verksamhet. Dalatravet Rättvik (DT Rättvik) används som en fallstudie. Resultaten baseras dels på registerdata över faktiskt registrerade hästar med koppling till DT Rättvik dels på enkätmaterial insamlat från ett urval av besökare vid tävlingar under sommaren 2014. Totalt används svar från 444 besökare i analysen. Resultaten från beräkningarna visar att den direkta ekonomiska effekten av DT Rättviks 333 travhästar i amatör- eller proffsträning genererar 20 miljoner i travhästtrelaterad konsumtion. DT Rättviks travsportevenemang uppskattas generera ca 29 miljoner varav 11,1 miljoner ärett tillskott till den regionala ekonomin i form av turistekonomisk omsättning. Travsportens sammanlagda regionalekonomiska effekt beräknas således uppgå till minst 31,1 miljonerkronor. Inkluderas även utbildning, avel och uppfödning samt sysselsättning vid ATG-ombudså uppskattas travsporten generera ca 40 helårsverken inom DT Rättviksregionen.  

Benchmark Evaluation of HOG Descriptors as Features for Classification of Traffic Signs

The purpose of this paper is to analyze the performance of the Histograms of Oriented Gradients (HOG) as descriptors for traffic signs recognition. The test dataset consists of speed limit traffic signs because of their high inter-class similarities.   HOG features of speed limit signs, which were extracted from different traffic scenes, were computed and a Gentle AdaBoost classifier was invoked to evaluate the different features. The performance of HOG was tested with a dataset consisting of 1727 Swedish speed signs images. Different numbers of HOG features per descriptor, ranging from 36 features up 396 features, were computed for each traffic sign in the benchmark testing. The results show that HOG features perform high classification rate as the Gentle AdaBoost classification rate was 99.42%, and they are suitable to real time traffic sign recognition. However, it is found that changing the number of orientation bins has insignificant effect on the classification rate. In addition to this, HOG descriptors are not robust with respect to sign orientation.

To what extent are CO2 emissions from intra-urban shopping trips by cars affected by drivers’ travel behaviour and store location?

Transportation is seen as one of the major sources of CO2 pollutants nowadays. The impact of increased transport in retailing should not be underestimated. Most previous studies have focused on transportation and underlying trips, in general, while very few studies have addressed the specific affects that, for instance, intra-city shopping trips generate. Furthermore, most of the existing methods used to estimate emission are based on macro-data designed to generate national or regional inventory projections. There is a lack of studies using micro-data based methods that are able to distinguish between driver behaviour and the locational effects induced by shopping trips, which is an important precondition for energy efficient urban planning. The aim of this study is to implement a micro-data method to estimate and compare CO2 emission induced by intra-urban car travelling to a retail destination of durable goods (DG), and non-durable goods (NDG). We estimate the emissions from aspects of travel behaviour and store location. The study is conducted by means of a case study in the city of Borlänge, where GPS tracking data on intra-urban car travel is collected from 250 households. We find that a behavioural change during a trip towards a CO2 optimal travelling by car has the potential to decrease emission to 36% (DG), and to 25% (NDG) of the emissions induced by car-travelling shopping trips today. There is also a potential of reducing CO2 emissions induced by intra-urban shopping trips due to poor location by 54%, and if the consumer selected the closest of 8 existing stores, the CO2 emissions would be reduced by 37% of the current emission induced by NDG shopping trips.

Young peoples’ entrance to the workplace : Introduction to occupational health and safety

Negative outcomes of a poor work environment are more frequent among young workers. The aim of the current study was to study former pupils’ conditions concerning occupational health and safety by investigating the workplaces’, safety climate, the degree of implementation of SWEM and the their introduction programs. Four branches were included in the study: Industrial, Restaurant, Transport and Handicraft, specialising in wood. Semi-structured dialogues were undertaken with 15 employers at companies in which former pupils were employed. They also answered a questionnaire about SWEM. Former pupils and experienced employees were upon the same occasion asked to fill in a questionnaire about safety climate at the workplace. Workplace introduction programs varied and were strongly linked to company size. Most of the former pupils and experienced employees rated the safety climate at their company as high, or good. Employers in three of the branches rated the SWEM implemented at their workplaces to be effective. The Industry companies, which had the largest workplaces, gave the most systematic and workplace introduction for new employees. There are no results from this study explaining the fact that young workers have a higher risk for workplace accidents.

Decomposing Consumer Wealth Effects: Evidence on the Role of Real Estate Assets Following the Wealth Cycle of 1990-2002

During the period of 1990-2002 US households experienced a dramatic wealth cycle, induced by a 369% appreciation in the value of real per capita liquid stock market assets followed by a 55% decline. However, consumer spending in real terms continued to rise throughout this period. Using data from 1990-2005, traditional life-cycle approaches to estimating macroeconomic wealth effects confront two puzzles: (i) econometric evidence of a stable cointegrating relationship among consumption, income, and wealth is weak at best; and (ii) life-cycle models that rely on aggregate measures of wealth cannot explain why consumption did not collapse when the value of stock market assets declined so dramatically. We address both puzzles by decomposing wealth according to the liquidity of household assets. We find that the significant appreciation in the value of real estate assets that occurred after the peak of the wealth cycle helped sustain consumer spending from 2001 to 2005.

Globalization, Agency, and Institutional Innovation: The Rise of Public-Private Partnerships in Global Governance

Public and private actors increasingly cooperate in global governance, a realm previously reserved for states and intergovernmental organizations (IOs). This trend raises fascinating theoretical questions. What explains the rise in public-private institutions and their role in international politics? Who leads such institutional innovation and why? To address the questions, this paper develops a theory of the political demand and supply of public-private institutions and specifies the conditions under which IOs and non-state actors would cooperate, and states would support this public-private cooperation. The observable implications of the theoretical argument are evaluated against the broad trends in public-private cooperation and in a statistical analysis of the significance of demand and supply-side incentives in public-private cooperation for sustainable development. The study shows that public-private institutions do not simply fill governance gaps opened by globalization, but cluster in narrower areas of cooperation, where the strategic interests of IOs, states, and transnational actors intersect.