Could climbing up Maslow's pyramid help us solve the world's environmental problems?: A study of the impact of human development from material to non-material needs on the environment
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Bibliographic record
Abstract
Our planet is witnessing an unprecedented increase in temperature that is causing frequent damages such as extreme weather conditions, acidification of oceans and extinction of species that may soon become irreparable. Various studies have reported that greenhouse gas emissions (GHG) from human activities in industries that produce 'material goods' like transportation, energy, manufacturing and agriculture have been the chief drivers of the climate change crisis. Even as greener and renewable energy technologies are increasingly being adopted, since consumption of material goods in the rich parts of the world continue to increase, it may not be enough to solve the environmental problems in time. However, we may be able to solve them by adopting a different way of living. In order to do so, we need to understand the purpose of the economy. According to Aristotle, Eudaimonia or the 'good life’ which is the full development of human capabilities or virtues is the ultimate end of human life and the purpose of the economy or the production and consumption of material goods by people are simply a means towards this ultimate end. A similar idea that human beings have an ultimate end towards which all actions are directed can also be found with the more popularly known Maslow’s hierarchy of needs. This essentially means that human beings need material wealth only insofar that it supports the fulfilment of their non-material needs: the development of capacities/virtues. 'Non-material' can refer to needs such as enjoying a classical dance performance, or studying philosophy or engaging in therapy. This perspective allows us to rethink growth in terms of a shift from material to non-material needs, which could also help solve the world's environmental problems as long as non-material needs can be met in less polluting ways (as compared to how material needs are met). Following this, economic activities are classified as physical (or goods-producing) or non-physical. Activities in the physical economy meet material needs and have a higher potential for productivity growth. Activities in the non-physical economy, including health care, education, research and arts, meet non-material needs and have a lower potential for productivity growth since human work is generally the final output. Based on this classification, it is observed that, in the advanced economies, the share of the physical economy in total demand is getting smaller, while the share of the non-physical economy is getting bigger over time, reflecting human development from material to non-material growth. If the non-physical economy − which consists mainly of human work such as a doctor's advice, a lesson, a concert − is relatively less polluting, such a shift could mean good news for the environment. While this looks promising, a consequence of the lower potential for productivity growth in the non-physical economy is that their activities can be expensive. Baumol (1993), termed this the 'cost disease' and argued that people in the rich parts of the world can afford the expensive 'personal services' when funds resulting from the productivity gains in the physical economy (due to labour-saving innovations) are transferred to the non-physical economy. Yet, nowadays, the standard response to the 'cost disease' is to replace human work with technology through standardisation, computerisation and robotisation of hitherto human tasks as a way to minimise costs. Due to the nature of work in the non-physical economy, the growing technology/ material-intensity can affect the quality of the non-material value created, while it may also increase the pressure on the environment. This led me to investigate the impact of the rising technology-intensity in the non-physical economy on the quality of outcome and on the environment. One of the main activities in the non-physical economy, health care, is 'an art and a science' in the sense that it takes care of the health of a human being in order to enable him to develop intellectually and psychologically and it is not just about 'fixing' what is broken. Doctors, therapists and nurses are people who possess besides clinical knowledge, interpersonal skills such as understanding and empathy to carry out complex human interactions with patients. Yet, in recent times, especially in the United States, technologies such as electronic health records (EHR), e-prescribing, tele-medicine and health apps are increasingly being adopted to displace some of the creative work performed by health care professionals. The main drivers of such trends identified are: the privatisation of health care, the systems of 'Managed Care' and government measures such as the American Recovery and Reinvestment Act (ARRA) of 2009 that allocated funds to incorporate health information technologies in health care facilities across the country. Some of the common experiences found with the adoption of technology in health care are: a supplier-induced demand for drugs and medical technologies due to a 'fee-for-service' payment model for doctors, a 'technology arms race' between hospitals and ‘direct-to-consumer’ advertising of medicines and medical technologies. Some of the consequences of these trends found are: unnecessary testing leading to increased overall health care costs, 'physician deskilling' due to decreased clinical knowledge and psychological and health implications for patients due to less physician-patient interactions and more 'end-of-pipe' solutions. From a brief review, it is also found that the health care system in Canada is less technology-/material-intensive than the health care system in the U.S. with (roughly) the same quality of service which shows that different kinds of health care systems can coexist with one another. A second activity of the non-physical economy, education, is one that enhances the intellectual and spiritual development of students, guided by a curriculum. According to various studies, teachers are indispensable in this activity in terms of their personal knowledge, their pedagogical capacities and their ability to instill curiosity, enthusiasm, sympathy, and morality in students through complex and dynamic interactions. Yet, similar to health care, even the creative work of teachers are being displaced with Information and Communication Technologies (ICT) such as: talent management software, digital learning technologies, MOOCs, allegedly to improve quality of education. In the United States, some of the trends commonly found to promote such technologies are: a pay-for-performance model for teachers to improve productivity of student grades, a common core standard curriculum and standardized tests for students to get them ready for college and careers. Some of the consequences of the growing technology intensity in education are found to be: teacher deskilling, lack of evidence of improved student performances and psychological implications for students such as diminished social skills, lack of creative and original thinking etc. These findings suggest that the growing technology-intensity in health care and education may have not significantly improved the quality of service provided, especially since the nature of work in these activities are different. Besides this, they could also lead to a higher environmental burden. In this thesis, I investigate whether the latter is the case, which is done in two steps. First, I investigate whether the non-physical economy is less polluting than the physical economy. Next, I examine the environmental consequences of the rising technology-intensity of the non-physical economy. The empirical research method applied is the Environmentally Extended Input-Output (EE-IO) analysis, which is used to compute the direct and indirect environmental effects of the physical and the non-physical economy. Direct effects refer to the pollution recorded at the place where it arises (for example, emissions due to health care). Indirect effects refer to the pollution caused by industries that supply inputs to health care, and for which the health care sector (as sector of use) is (indirectly) responsible. Through an EE-IO analysis, environmental effects (such as emissions) are ascribed to the sector of use, by reallocating emissions from the sector where they originate (sector of origin) to the sector that uses the goods or services supplied by this sector. The EE-IO analysis makes use of an Input-Output (I-O) table which records intermediate deliveries of goods and services between sectors in an economy, as well as each sector's deliveries to final demand. In addition, it uses the environmental accounts of each sector to compute the total emissions for which a sector can be held responsible, which consist of its own (direct) emissions plus the emissions that are related to the inputs it purchases from other industries. The two equations that guide this computation are B=b*(1-A)¯¹ and E=B*f where B= a vector of total (direct+indirect) emission intensities (per sector); b=direct emission intensity vector; (1-A)¯¹= the 'Leontief Inverse matrix' (derived from an I-O table) that represents the technical coefficients or the total (direct+indirect) inputs required per unit of a sector's output; f=final demand and E=a vector of total (direct + indirect) emissions per sector for a given level of final demand f. The results found were that the non-physical economy in the United States is less polluting in terms of global warming and acidification potential, energy use and tropospheric ozone gas emissions, even if emissions are re-allocated from sector of origin to sector of use. This result suggests that the impact on the environment will be lower if human beings become increasingly interested in non-material rather than material growth. However, this result may not come about if the non-physical economy gets more technology-intensive. To empirically investigate the increase in technology-intensity in
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it