Economic Implications of Environmental Sustainability for Companies: A Case Study of 3M

As awareness of sustainability grows, firms are being pressured to adopt social and environmental practices to keep pace with ethical standards and consumer demand. Firms must adapt to a changing marketplace, and new management strategies are being developed. Our central purpose in this paper is therefore to explore the economic implications of enhanced environmental sustainability through a case study of 3M, a chemical company that has been implementing sustainable solutions for over 30 years. We begin our case study by analyzing the effectiveness of the lifecycle management approach (LCM) currently advocated to businesses in search of sustainability. Although the LCM methodology is still developing at this stage, it has yielded great results for 3M when combined with employee expertise. We will then go on to analyze why these increases in sustainability have increased profits, and what effect tighter environmental legislation would have on competitive markets. The final section of this paper will analyze the performance of environmentally responsible firms on the stock market to determine whether increased sustainability makes firms more desirable to investors. Our critical analysis of the multi-faceted economic implications of enhanced environmental sustainability will therefore allow us to determine 1) the effectiveness of current approaches to sustainability; 2) the economic implications of enhanced corporate responsibility and legislation, and 3) the impact of enhanced sustainability on the performance of companies on the stock market

Maths4Stats: Opleiding vir onderwysers

Maths4Stats: Educating teachers. The inadequate nature of the education infrastructure in South Africa has led to poor academic performance at public schools. Problems within schools such as under-qualified teachers and poor teacher performance arise due to the poorly constructed education system in our country. The implementation in 2012 of the Curriculum and Assessment Policy Statement (CAPS) at public schools in South Africa saw the further crippling of some teachers, as they were unfamiliar with parts of the CAPS subject content. The Statistics and Population Studies department at the University of the Western Cape was asked to join the Maths4Stats project in 2012. This project was launched by Statistics South Africa in an effort to assist in training the teachers in statistical content within the CAPS Mathematics curricula. The University of the Western Cape's team would like to share their experience of being part of the Maths4Stats training in the Western Cape. This article focuses on how the training sessions were planned and what the outcomes were. With the knowledge gained from our first Maths4Stats experience, it is recommended that future interventions are still needed to ensure that mathematics teachers become well-informed and confident to teach topics such as data handling, probability and regression analysis

Tundra photosynthesis captured by satellite-observed solar-induced chlorophyll fluorescence

Accurately quantifying the timing and magnitude of respiration and photosynthesis by high‐latitude ecosystems is important for understanding how a warming climate influences global carbon cycling. Data‐driven estimates of photosynthesis across Arctic regions often rely on satellite‐derived enhanced vegetation index (EVI); we find that satellite observations of solar‐induced chlorophyll fluorescence (SIF) provide a more direct proxy for photosynthesis. We model Alaskan tundra CO2 cycling (2012–2014) according to temperature and shortwave radiation and alternately input EVI or SIF to prescribe the annual seasonal cycle of photosynthesis. We find that EVI‐based seasonality indicates spring “green‐up” to occur 9 days prior to SIF‐based estimates, and that SIF‐based estimates agree with aircraft and tower measurements of CO2. Adopting SIF, instead of EVI, for modeling the seasonal cycle of tundra photosynthesis can result in more accurate estimates of growing season duration and net carbon uptake by arctic vegetation

Effect of retention interval on showup and lineup performance

Showups – when a single suspect is presented to an eyewitness – are thought to be a more suggestive procedure than traditional lineups by the U.S. Supreme Court and social science researchers. The present experiment examined the impact of retention interval on showup identifications, because immediate showups might be no worse than, and perhaps even better than, a lineup conducted after a delay. Participants (N = 1584) viewed a mock-crime video and then were presented with a showup or a simultaneous lineup, either immediately or a 48 h delay. Receiver operating characteristic (ROC) analyses revealed that a showup never resulted in better identification accuracy than a lineup. We conclude with a discussion of whether showups should ever be used

Eyewitness confidence: social influence and belief perseverance

Two studies investigated changes in eyewitness identification confidence after an identification has been made. In Experiment 1, thefts were staged 70 times for pairs of unsuspecting eyewitnesses (total n = 140). Before campus security arrived, witnesses were separated and attempted identifications of the thief from a target-absent photospread. Biased instructions were used to induce false identifications. The 96% who made false identifications were then randomly assigned to 1 of 9 conditions telling them of the alleged identification decision of their co-witness. Witnesses were told that (a) the co-witness identified the same person, (b) the co-witness identified a different but plausible other person, (c) the co-witness identified an implausibly different person, (d) the co-witness rejected the photospread, or (e) the witness was told nothing about the co-witness\u27s decision. In addition, some witnesses who were told that the co-witness identified the same person were later told that the information was incorrect and that the co-witness had actually identified a different person or were told that it was not known who the co-witness identified. As well, some witnesses who were told that the co-witness had identified a different person were later told that the information was incorrect and that the other witness had actually identified the same person or were told that it was not known who the co-witness identified. Following the co-witness information manipulation, a campus security officer questioned witnesses about their identifications of the thief. Compared to the no-information control condition, co-witness agreement produced a robust inflation of certainty whereas co-witness disagreement produced a precipitous decline in certainty. With one exception, correcting the co-witness information did not eliminate the confidence-inflating and deflating effects of the original information, indicating a strong perseverance effect. In the second experiment, subject-jurors (n = 378) viewed the testimony videotapes and evaluated the credibility of the witnesses. Subject-jurors\u27 ratings of perceived credibility were influenced by the type of information witnesses had received in a pattern that generally paralleled the results of Experiment 1

Improving estimates of net ecosystem CO2 exchange between the Arctic land surface and the atmosphere

Feedbacks between the climate system and the high-latitude carbon cycle will substantially influence the intensity of future climate change. It is therefore crucial that the net ecosystem exchange of CO2 (NEE) between the high-latitude land surface and the atmosphere is accurately quantified, where NEE refers to the difference between ecosystem respiration (R) and photosynthesis (gross ecosystem exchange, GEE): NEE=-GEE+R in umol/m^2/s. NEE can only be directly measured over areas of 1 km^2 through eddy covariance, and modeling approaches such as the Vegetation Photosynthesis Respiration Model (VPRM) are required to upscale NEE. VPRM is a remote sensing based model that calculates R as a linear function of air temperature (Ta) when air temperature is above a given threshold (Tlow), and sets respiration to a constant value when Ta<Tlow. GEE is estimated according to remote sensing observations of vegetation indices, shortwave radiation, air temperature, and soil moisture. Although in situ findings have shown that snow and Arctic species composition have a substantial influence on high-latitude NEE, model estimates of high-latitude NEE have typically been generated without Arctic-specific vegetation classes, and without using remote sensing observations to represent the effects of snow on NEE. The hypothesis driving this work was therefore that uncertainty in estimates of high-latitude NEE could be reduced by representing the influences of Arctic vegetation classes and snow. The central objectives were to determine feasible approaches for reducing uncertainty in VPRM estimates of NEE by representing the influences of snow and Arctic vegetation, create PolarVPRM accordingly, and analyze inter-annual variability in PolarVPRM estimates of high-latitude North American NEE (2001-2012). The associations between snow and NEE, and the potential to describe these influences on NEE using remote sensing observations, were examined using time lapse camera observations of snow cover area (SCA) and eddy covariance measurements of NEE from Daring Lake, Northwest Territories, Canada. Analyses indicated good agreement between SCA derived from camera, Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) observations. SCA was also found to influence the timing and magnitude of NEE. MODIS SCA was therefore incorporated into VPRM, and VPRM was calibrated using eddy covariance and meteorological observations collected in 2005 at Daring Lake. VPRM was run through years 2004-2007 over both Daring Lake and Ivotuk, Alaska, USA, using four model formulations, three of which represented the effects of SCA on respiration and/or photosynthesis, and another which did not use MODIS SCA. Comparisons against eddy covariance observations indicated that uncertainty was reduced in VPRM estimates of NEE when respiration was calculated as a linear function of soil temperature when SCA>50%, and as a linear function of air temperature when SCA<50%, thereby reflecting the influence of snow on decoupling soil/air temperatures. Representing the effect of SCA on NEE therefore reduced uncertainty in VPRM estimates of NEE. In order to represent spatial variability in high-latitude estimates of NEE due to vegetation type, Arctic-specific vegetation classes were created for PolarVPRM by combining and aggregating two existing vegetation classifications: the Synergetic Land Cover Product and the Circumpolar Arctic Vegetation Map. Levene's test indicated that the PolarVPRM vegetation classes divided the pan-Arctic region into heterogeneous distributions in terms of net primary productivity, and passive microwave derived estimates of snow and growing season influences on NEE. A non-parametric statistical approach of Alternating Conditional Expectations found significant, non-linear associations to exist between passive microwave derived estimates of snow and growing season drivers of NEE. Furthermore, the shape of these associations varied according to the vegetation class over which they were examined. Further support was therefore provided to the idea that uncertainty in model estimates of NEE could be reduced by calculating snow and growing season NEE separately within each vegetation class. PolarVPRM estimates of NEE in 2001-2012 were generated at a three hourly and 1/6 x 1/4 degree resolution across polar North America (55-170 W, 55-83 N). Model calibration was conducted over three sites: Daring Lake, Ivotuk, and Atqasuk, Alaska, USA. Model validation was then conducted by comparing PolarVPRM estimates of year-round daily average NEE to non-gap-filled eddy covariance observations of daily average NEE acquired over the three calibration sites, as well as six other Arctic sites. PolarVPRM performed well over all sites, with an average mean absolute error (MAE) of 0.20 umol/m^2/s, and had diminished error rates when the influence of SCA on respiration was explicitly represented. Error analysis indicated that peak growing season GEE was underestimated at Barrow because GEE at this site showed a stronger response to the amount of incoming shortwave radiation than at the calibration site, suggesting that PolarVPRM may underestimate GEE over wetland and barren vegetated regions. Despite these uncertainties, PolarVPRM was found to generate more accurate estimates of monthly and three-hourly NEE relative to eddy covariance observations than two established models, FLUXNET Model-Tree Ensemble (MTE) and CarbonTracker. Relative to eddy covariance observations and PolarVPRM estimates, MTE tended to overestimate snow season respiration, and CarbonTracker tended to overestimate the amount of midday photosynthesis. Analysis of PolarVPRM output across North America (north of 55 N) found an increase in net annual carbon efflux over over time (2001-2012). Specifically, increased rates of respiration are estimated when soil and air temperatures are warmer. Although increases in growing season vegetation indices and air temperature enable greater photosynthetic uptake by Arctic vegetation, forests and shrublands uptake less CO2 in the middle of the growing season when air temperatures rise above the physiological optima for photosynthesis. As a result, PolarVPRM estimated a decline in net photosynthetic uptake over time. Overall, PolarVPRM output indicates that North American regions north of 55 N are losing strength as a carbon sink in response to rising air temperatures.1 yea

Power and influence of IT and HR departments within the manufacturing industry

Academic literature found that HR and IT departments often lack power and influence within their organisations leading to sub-optimal performance of the organisations. The purpose of this research was to explore power and influence of HR and IT departments within the manufacturing industry, so as to gain an understanding of the enablers and inhibitors driving these levels. This will enable managers and executives to make better use of these drivers in order to improve not only the performance of the HR or the IT department but also overall business performance. A qualitative exploratory approach was adopted to identify new insights into the power and influence of the HR and IT departments. Semi-structured, in-depth interviews were conducted with managers and executives within an HR or an IT department, covering eight sectors within the manufacturing industry, located in the Gauteng area, South Africa. Thematic content analysis was used to analyse each interview. This study found that departments are perceived to be in a position of power and influence when they have a formal form of power, such as legitimate power or when they make use of legitimating tactics to influence others. HR departments often lack power and influence. Some IT departments have power and influence, and some do not. This is ascribed to the transforming of IT department power and influence due to the rapidly changing IT environment. The CEO, top management, expertise, metrics, technology and policies and procedures were identified as constructs enabling or inhibiting the power and influence of both HR and IT departments. These constructs affect the credibility of a department and impact on the co-operation and integration with other departments. The findings of this study add to the literature in the field of power and influence.Mini Dissertation (MBA)--University of Pretoria, 2018.zk2019Gordon Institute of Business Science (GIBS)MB