Technological change, industry structure and the environment

Futures 36 (2004) 267–293 www.elsevier.com/locate/futures

Technological change, industry structure and the environment Paul Dewick , Ken Green, Marcela Miozzo Manchester School of Management, UMIST, PO Box 88, Manchester M60 1QD, UK

Abstract This paper contributes towards the construction and application of a method to assess the long-term impact of the development of pervasive technologies on the environment. It seeks to integrate insights from studies of technology regarding long-term growth with questions of sustainability. Using a methodology based on long-wave theory and a sector classification based on technological characteristics, the likely effects of the three pervasive technologies (information technology, biotechnology and nanotechnology) on the input-output structure of selected sectors and on the levels of emissions of industrial greenhouse gases are considered. # 2003 Elsevier Ltd. All rights reserved.

1. Introduction The dynamics and evolution of industries are bound to have important implications for the future state of the environment. Although our knowledge is still meagre in terms of the emergence, development and decline of different industries, entry and exit, growth and decline of firms [23,29], a number of contributions have attempted to explain the systematic forces that explain the evolution of technologies, firms and industry structure and of supporting industries. In particular, the debates on ‘long waves’ explore the long run properties of the evolution of industries [13]. Also, the theory of ‘industry life cycle’ rationalises the natural evolution of broad technologies [1]. It has also been suggested that there are relations between the dynamics of an industry and the rate of technical change [30] and a 

Corresponding author. Tel.: +44-161-275-0456; fax: +44-161-200-3505/8787. E-mail address: [email protected] (P. Dewick).

0016-3287/$ - see front matter # 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0016-3287(03)00157-5

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first attempt to put some order in the diversity of sectoral patterns of technical change was the taxonomy of Pavitt [34]. However, these insights on the evolution of technologies and industries have not been used to explore the effects on the environment. Indeed, as argued by the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios [21]: ‘Economists and others who study technological change have developed models that take a variety of dynamics into account. . . Some models focus on technologies themselves, for example, examining the various sources of ‘increasing returns to scale’ and ‘lock-in’. . . Other models focus on firms and other decision-makers, and their process of information assimilation, imitation, and learning. . . Few of these dynamics, apart from ‘increasing returns to scale’, have been applied to the projection of greenhouse gas emissions from the energy sector’ (p.141). This paper attempts to remedy such omission, by considering the insights from studies of technological change on the evolution of technologies that are not directly concerned with energy generation or efficiency (i.e. ‘non-energy’ technologies) and their effect on the environment. In attempting to incorporate insights from the theories of the dynamics and evolution of technologies, there are important points to make about four aspects of economic activity: the emergence, development and decline of different sectors/industries; the location of industry (and subsequent country specialisation); industry structure; and patterns of trade. These processes, such as changes in main branches of production (e.g. from ‘polluting’ goods handling industries to ‘clean’ information handling industries and shifts in geographic structure of production and trade (e.g. the pattern of trade of machinery and equipment between developed and developing countries) have important effects on the environment through greenhouse gas emissions from production and transportation; though these will differ by country and region [4,33]. These differences in environmental effects by regions and countries are important to capture, given the popularity of proposals for trade in pollution rights, for example. This paper will assess the impact of technological change on the structure of production in selected sectors drawn from a sectoral taxonomy of patterns of technical change in terms of changes in input structure and subsequent effects on output and resource efficiency. The paper will seek to provide answers to the following questions: will pervasive technologies lead to an increase or decrease in inputs from other industries?, and, as a result of the application of pervasive technologies, will output increase or decrease and will production techniques be more or less resource intensive? The paper describes a qualitative method to assess the impact on the structure of production of technologically different industries of pervasive technologies that might change the level of greenhouse gas emissions to 2050. The effects of technological change will not be the same across all industries in broad categories of sectors, however, by analysing the effects on examples of sectors

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across broad categories one can draw general implications for the likely effects across the broad categories, particularly in terms of the direction and magnitude of changes. The paper does not try to quantify the effects of technological change on the level of industrial greenhouse gas emissions—this will be the focus of a future study—but, by analysing the potential changes in input structure, suggests the direction of effects on output and greenhouse gas emissions. The paper is structured as follows. The next section describes the methodology by which the technologies likely to become prevalent during the first half of the twenty-first century were assessed. It draws on the concept of ‘long waves’ (or Kondratieff cycles), in which each economic wave is marked by a key cluster of technologies, the development of which is accompanied by the establishment of an appropriate and supportive structure of institutions (which together have been called the ‘techno-economic paradigm’) and which drive economic growth. The paper briefly outlines the current ‘long wave’ but focuses on the two additional emerging technologies likely to have a pervasive effect over the next 50 years. Then the paper reports the results of the application of a method to assess the impact of developments in the three pervasive technologies on the resource intensity and structure of production across examples of industries from four of the eight broad technological sectors in terms of their greenhouse gas emissions up to 2050. A final section draws implications for future work in this area. 2. Long waves and pervasive technologies up to 2050 We seek to identify the impact of the development of pervasive technologies on the production of a number of different industries and the consequent effects on their energy demand and industrial greenhouse gas emissions. The sectors are differentiated by their technological characteristics, identified and classified by Pavitt [34], modified to include service sectors by Soete and Miozzo [42] (see also [27]). Pavitt [34] argues that patterns of innovation are cumulative and that the technological trajectories of innovating firms are largely determined by what they have done in the past as well as their current principal activities. Pavitt [34] differentiates between ‘supplier dominated’ sectors, ‘production intensive’ sectors—subdivided into ‘scale intensive’ and ‘specialised equipment suppliers’—and ‘science based’ sectors. These generate different technological trajectories determined by sectoral differences in three characteristics: the source of the technology used, users’ needs and the means of appropriating benefits. The sectors differ as to whether technological change is product- or process-centred, internally or externally generated, radical or incremental in nature; there are also differences in the methods of appropriation of innovation, and technological diversification of innovating firms. Whereas Pavitt’s [34] taxonomy classified services as supplier-driven, Soete and Miozzo [42] show that innovative practices in services firms can be fitted into a slightly modified set of categories. Service sectors, like manufacturing sectors, can be categorised as ‘supplier dominated’ sectors, ‘scale-intensive’ sectors (which can be further sub-divided into ‘physical networks’ and ‘information networks’) and ‘specialised suppliers and science based’ sectors. Table 1 shows a sectoral

Typical core sector

In-house production engineering, Suppliers, R&D

Price sensitive

In-house, Customers, Services suppliers

R&D, Public science, In-house production engineering, Suppliers

Performance sensitive

Mixed

Small, Large

Small

Size of innovating firms

Cost-cutting, Networking Cost-cutting, Networking

Product design

Small

Large

Large

Large

Small

Cost cutting (product Large design)

Product design, Improving performance

Cost-cutting

Technological trajectory

R&D know-how, Mixed Patents, Process Secrecy and know-how, Dynamic learning economies R&D, Know-how, System design Skills, Copyright, Product differentiation

Standards, Norms

Process secrecy and know-how, Technical lags, Patents, Dynamic learning economies Design know-how, Knowledge of users, Patents Standards, Norms

Non-technical TM, marketing, advertising, design Non-technical, Not allowed

Means of appropriation

Note: Rows in bold indicate sectors analysed further in the paper (see Sections 3.1–3.4). Source: adapted from Pavitt (1984) [34] and Miozzo and Soete (2001) [27].

Software, Specialised Business Services

Scale Intensive Physical Networks Scale Intensive Information Networks Science Based Gen- Pharmaceuticals eral

Specialised Suppliers/Science Based

Price sensitive

Suppliers, Govn R&D, Large clients Manufacturing suppliers, Performance sensitive, Manufacturing and ser- Quality sensitive vices suppliers

Type of user

Sources of technology

In-house design and Performance sensitive development users/ customers Transport, Wholesale In-house, Manufactur- Price sensitive ing suppliers Finance, Insurance, In-house, manufacturing Price sensitive Communications and services suppliers

Agriculture, Housing, Traditional manufacturing Supplier Dominated Personal services (resServices taurants, laundry), Public and social services (health, education) Scale Intensive Gen- Bulk materials (steel, eral chemicals, glass), Assembly (consumer durables, cars) Specialised Suppliers Machinery, InstruGeneral ments

Supplier Dominated General

Category of firm

Table 1 Technological taxonomy of sectors

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taxonomy combining the classifications of Pavitt [34] and Soete and Miozzo [42], which groups firms into eight sectors, with examples of core sectors and a description of their technological characteristics. We use an aggregated taxonomy for two main reasons. First, it is unrealistic to analyse the likely long-term effect of technological change on the sectors included today in the Standard Industrial Classification (SIC) industries for 2050. Second, although we do not know what the industrial structure will be in the future, we do know that it will change; sectors will grow or shrink, some will die and new industries will emerge. Simply by looking back 50 years one can identify products (microwaves, personal computers), production processes (automation, robotics) and entire industries (computer services, telecommunications) that either did not exist or have radically transformed over the period. By aggregating the industries into broad technological categories, we can consider the likely effects of technological change on firms in those sectors: the eight categories will remain in the future even if the industries that currently define the sectors do not. To assess the likely effects on the production systems of technological change over the next 50 years, ‘long-wave’ theory points to the need to identify the key elements of future ‘techno-economic paradigms’ [13,35]. These key elements would be significantly different from those in current paradigms and affect inputs, cost structures and the conditions of production and distribution. The key elements are also associated with a ‘cluster’ of inter-related radical and incremental innovations (product, process, technical, organisational and managerial innovations) that have a so-called ‘pervasive’ effect throughout the whole economy. We know that the key elements of all previous Kondratieff cycles developed in the techno-economic paradigm that preceded each one. This suggests therefore that we might be able to identify the key elements of future paradigms within industries that are in their infancy today. The fifth Kondratieff cycle, the current long wave that began in the 1970s/1980s, is built on the core technological input of microelectronics. One can identify the new infrastructure (telecommunications and the internet), carrier branches (computers and software) and associated organisational changes (such as ‘network’ firms) and other features of national systems of innovations derived from this key input [12]. The key factor involved in this information and communication technology and this Kondratieff wave will not be superseded until it (and its cluster) shows distinct signs of diminishing returns and limits to productivity growth. ‘Long wave’ theory suggests that biotechnology and nanotechnology may be key inputs in the next Kondratieff wave. There is evidence of their pervasive application, falling relative cost and increased availability. Biotechnology had its roots in 1960s biomedicine [41] but the biotechnology industry has been growing rapidly and biotechnology firms became financially stronger in the 1990s and 2000s. In 2000, for example, US biotechnology firms raised $33bn on Wall Street (more than the previous five years combined) and remained fairly unscathed by the global downturn with the AMEX Biotech Index outperforming the Nasdaq composite and Dow Jones Industrial Average between July 1999 and June 2001 [10]. Interestingly, as internet firms (measured by the AMEX Internet Index) dependent on the

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manipulation of information, fell by 39% over the two years to June 2001, biotechnology companies (measured by the AMEX Biotech Index) increased by 177% [10]. Biotechnology has the potential to create many generic platforms and have a pervasive effect across a wide range of industries: pharmaceuticals, health care diagnostics, agriculture, food, materials technology, energy, and environmental monitoring [7]. Characteristic of an industry in its infancy, most economic effects are concentrated on the supply side as the core science is developed and the key technologies mastered [7]. There are many overlaps between biotechnology and information technology; without a mature information and communication technology system facilitating distributed working for product development, biotechnology could not have grown so fast. Nanotechnology is another fledgling technology that had its birth within the information technology wave.1 Currently, the relative infancy of nanotechnology can be compared to information technology in the 1960s and biotechnology in the 1980s. Characteristically, governments, universities and large multinational firms are undertaking most of the work into nanotechnology. Early developments during the 1980s and 1990s have led to significant government funded projects in not only the developed countries (e.g. USA, Japan and Europe) but also in countries not usually in the vanguard of technological development (e.g. China).2 In the private sector, some big spending research companies, such as Hewlett Packard, IBM and 3M, are currently devoting one third of their research budgets to nanotechnology [31]. In addition, there are an estimated 470 nanotechnology companies in the USA, Europe and Asia, half of which are based in the US [11]. At present, nanotechnology has much potential but few applications. Indeed, although nanotechnology’s most pervasive impacts seem some way off, probably after 2020, it is likely that some nanotechnology products will make an impact on the industrial scene before then (some commentators, notably Miles and Jarvis [24], believe as soon as 2006). Nanotechnology can best be divided into top down and bottom up technology. To date, nanotechnology development is focused on top down technology, the fabrication of nanoscale structures by machining and 1 Nanotechnology can be defined as a technology dealing with materials and systems that have three properties: at least one dimension of between 1 to 100 nanometres (a nanometre is 100 billionth of a metre), designed through a process that exhibits fundamental control over the chemical and physical attributes of molecular scale structures, and capable of being combined to form larger structures [43]. 2 In the USA, for example, the National Nanotechnology Initiative, launched in 2000, provided funding of US$422m in 2001 (US$604m estimated in 2002, US$710 proposed in 2003) across 10 government departments; the bulk of the funds channelled toward the National Science Foundation and the Departments of Defence and Energy [9]. Japan has what is generally considered to be the only fully co-ordinated funded national policy of nanotechnology (the most prominent product of which has been the programme on atomic manipulation, 1991–2001, ‘Research and Development of Ultimate Manipulation of Molecules’) and nanotechnology is one of the four strategic platforms for Japan’s second basic plan for science and technology [25]. The European Commission funds nanoscience through the 6th Framework and national states (particularly Germany and the UK) fund significant nanotechnology programmes [5]. In the Far East, China is second only to Japan in terms of public funding of nanotechnology in 2002 [38].

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etching [40]. The future of nanotechnology, however, is in bottom-up technology (also called molecular nanotechnology), where organic and inorganic structures are created atom by atom, molecule by molecule [40]. Nanotechnology research and development is currently focused on novel materials (such as carbon nanotubes, quantum dots) and fabrication tools and instrumentation (top down methods such as scanning probe microscope, lithographics; bottom up methods such as selfassembly; software modelling; measurement tools (nanometrology)) [9,11,36,40]. These developments are likely to have applications in informatics (e.g. electronics, magnetics and optics—moving beyond Moores Law with molecular nanoelectronics is probably the most anticipated application in informatics), pharmaceuticals and medicine, energy and defence [5,9,18,24,28,36]. It is fair to assume that applications in the short-term are likely to be concentrated in industries/areas of strategic significance, where performance is deemed more important than price (e.g. military, space and medical applications) or in areas where the potential market revenues are likely to be vast. Based on our current understanding of the role of technological change in economic development in the long term, it is reasonable to assume that, during the next 50 years, the most significant technological developments will be energy technologies, information technology, biotechnology and (to a lesser extent) nanotechnology. Existing modelling work includes changes in emissions brought about by the introduction of new energy technologies (e.g. see IPCC [21]; but the impact of other technological changes, and in particular the impact of the other three pervasive technologies (information technology, biotechnology and nanotechnology), has not yet been estimated. In late 2001, we co-ordinated a workshop involving a structured exchange between a panel of experts from UMIST and Manchester University.3 The participants involved were experts on the role of technological change in economic development; some had an awareness of climate change issues and experience of scenario-based analysis. Prior to the event, the authors provided the participants with notes explaining the aims of the workshop, the industrial aggregation into the eight sectors, an indication of current greenhouse gas emissions of the different sectors and assumptions regarding the technologies likely to be pervasive over the next 50 years. Participants were first asked to identify the most pervasive technologies in the next 50 years and then consider their impact on changes in the structure of production and consequently on greenhouse gas emissions. The group of experts suggested a slightly revised classification of the technologies that would be key factors in the next Kondratieff waves focusing on the trajectories of knowledge development that underlie the emergence of specific technologies. Thus, likely technological developments of the next few decades are likely to be concerned with manipulations of organisms, of information and of materials. Materials technologies, for example, are historically one of the most 3 This type of method had been previously used by the ESRC Centre for Research on Innovation and Competition (CRIC) as part of a DTI study into ‘success’ in the field of biotechnology and information and communication technologies in the UK by 2005 [7,8].

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pervasive, though it is difficult to attribute developments in materials technology to any one technology or sector, particularly when there has been considerable convergence between technologies and when the trajectories of mechanisation and automation are different across industries. For example, there are materials technologies being developed using knowledge and applications of biotechnology (e.g. in the application of genetic modification technologies to the production of biomass for energy, food and chemicals) and nanotechnology (e.g. carbon nanotubes) in addition to developments outside of the information technology, biotechnology and nanotechnology categories (e.g. memory plastics). Developments in what we currently call biotechnology are an expression of developments in organism manipulation, though they include the rapidly developing scientific areas (and the technologies that might emerge from these) of genomics, proteomics and metabolomics. Similarly, nanotechnology, to differentiate its effects from information technology or biotechnology materials, should be seen more in terms of materials manipulation at the nano-scale; in the short-medium term this should constitute top-down technologies, in the longer-term, it will be based on bottom-up technologies. To sum up, the experts agreed that up to 2050, the most pervasive activities will be in information, organism and materials manipulation; but these manipulations will express themselves at different times in different technologies, most immediately called information technology, biotechnology and nanotechnology, though there will be other technologies in the future that are not currently embraced by these names. The next section examines the impact of these technologies on changes in the structure of production and greenhouse gas emissions. 3. Assessing the impact of pervasive technologies on selected sectors to 2050 This section explores the impact of information technology, biotechnology and nanotechnology on greenhouse gas emissions in four sectors selected from the eight sectors in the technological taxonomy in Table 1. Using data on output and CO2 emissions from the Global Trade Analysis Project (GTAP) Version 4 Database (see http://www.gtap.agecon.purdue.edu), an assessment of the current relationship between industrial structure and the environment can be made. The GTAP database contains industrial data on output and emissions for 18 sectors, aggregated from 50 industries.4 We re-categorised the 18 GTAP sectors into the eight sectors outlined in Table 1.5 We used the output and emissions data to create two graphs 4

Carbon dioxide figures are used to approximate total greenhouse gas emissions. This is a reasonable approximation since CO2 accounted for over 85% of UK greenhouse gas emissions in 2000 [6]. 5 Due to the aggregated nature of the GTAP data, four of the sectors were contained within two GTAP sectors. The GTAP ‘other market services’ sector includes telecommunications, finance and banking, which belong to the Scale Intensive Information Networks (SIIN) sector, as well as computer services and management consultancy, which belong to the Science Based Services (SBG) sector. Also, the GTAP ‘chemical products’ sector includes the chemicals industry, belonging to the Scale Intensive General (SIG) industry, and the pharmaceuticals industry, a Science Based General (SBG) sub-sector. Of these joint sectors, we analyse the SIG sector and must therefore differentiate between the effects of bulk chemicals and pharmaceuticals (see Section 3.3).

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Fig. 1. Percentage of total output by sector by region 1995. Key: SDG: Supplier Dominated General, SDS: Supplier Dominated Services, SIG: Scale Intensive General, SIIN: Scale Intensive Information Networks, SIPN: Scale Intensive Production Services, SSG: Specialist Supplier General, Source: Generated using data from GTAP Data Package Version 4.

showing the proportion of total output and emissions accounted for by each of the broad technological categories (see Figs. 1 and 2) and one table showing the resource efficiency (i.e. emissions per unit output) of the 18 GTAP sectors (see Table 2). The resource efficiency figure gives us an idea of how ‘clean’ production in a particular industry in each region is allowing us to differentiate between ‘clean’ and ‘dirty’ industries both within and between countries. Knowing the emissions levels associated with a set output also allows us to predict better the likely effect of technological change on production and emissions levels. For comparison purposes, we present data for Europe, USA and China. The diffusion of the pervasive technologies in a particular industry will be seen in changes in the level of inputs into that industry from all other industries. We use E3MG input-output data from Cambridge Econometrics for each of our sectors for the European Union, China and the USA, expressed in value terms. Although the same analysis has been conducted for all sectors and all regions, the effects of technological change in information technology, biotechnology and nanotechnology is presented below in Sections 3.1–3.4 for the following broad technological categories: the Supplier Dominated General (SDG) sector, the Supplier Dominated Services (SDS) sector, the Scale Intensive General (SIG) and Specialised Supplier General (SSG) sector. These four sectors, as shown in Figs. 1 and 2, accounted for between 55% and 82% of total country/region output and between 71% and 87% of total country/region emissions in 1995. Characteristic industries from these four

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Fig. 2. Percentage of total emissions by sector by region 1995. Key: SDG: Supplier Dominated General, SDS: Supplier Dominated Services, SIG: Scale Intensive General, SIIN: Scale Intensive Information Networks, SIPN: Scale Intensive Production Services, SSG: Specialist Supplier General, Source: Generated using data from GTAP Data Package Version 4.

Table 2 Resource efficiency of the eight aggregated sectors 1995 in CO2 per unit output Industrial Sectors

GTAP Sector

China

USA

EU 15

SDG SDG SDG SDG SDG SDG SDG SDS SIG SIG SIG SIG SIG SIG SIG SIIN or SBS SIPN SSG

01 Agriculture 02 Coal 03 Oil 04 Gas 05 Electricity 13 Construction 16 Textile Industry 18 Non Market Services 06 Ferrous and non ferrous metals 07 Chemical Products 08 Other energy intensive 09 Electronic Equipment 10 Transport equipment 12 Other Manufacturing products 14 Food Industry 17 Other Market Services 15 Trade and Transport 11 Other Equipment Goods

0.002497 0.008963 0.006407 0.158521 0.200377 0.002952 0.004504 0.009959 0.054041 0.012989 0.030263 0.001817 0.003693 0.012843 0.006514 0.005614 0.015238 0.006490

0.000816 0.003938 0.022407 0.026584 0.056002 0.001698 0.000625 0.000627 0.008334 0.003553 0.003259 0.000470 0.000601 0.000898 0.000556 0.000619 0.001929 0.000754

0.001755 0.001677 0.011550 0.013971 0.021706 0.000715 0.000521 0.000512 0.003530 0.001188 0.002135 0.000150 0.000520 0.000540 0.000565 0.000517 0.001762 0.000521

Key: SDG: Supplier Dominated General; SDS: Supplier Dominated Services; SIG: Scale Intensive General; SIIN: Scale Intensive Information Networks; SIPN: Scale Intensive Production Services; SSG: Specialist Supplier General. Source: Adapted using data from GTAP Data Package Version 4.

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sectors are chosen for further analysis: agriculture from the SDG sector, health services from the SDS sector, chemicals from the SIG sector and agricultural and industrial machinery from the SSG sector. We know what are currently the most important inputs into each of these four industries (in terms of value) in the three regions and can speculate about how inputs are likely to change as a result of technological change and the diffusion of the pervasive technologies over the next 50 years. Generally speaking, changes in the diffusion of information technology in each industry can be traced through changes in the current input structure, particularly in the office machines, computer services, telecommunications and electricals industries. Changes in the diffusion of biotechnology are likely to be reflected in inputs from sub-sectors of the pharmaceuticals, chemicals, and agriculture (e.g. seed industry) sector amongst others. The effects of future nanotechnology developments are more difficult to attribute to any specific sector. Sections 3.1–3.4 focus explicitly on changes to industry inputs (expressed as a percentage of total inputs) in the EU occurring as a result of technological change in the pervasive technologies. It is important to consider value changes in the input/output structure since technological change may have opposite effects in volume and price terms (i.e. inputs into an industry may increase but their price may be lower; the net effect will then depend on the magnitude of the output and price changes). Since we are considering the value changes, a number of observations about likely price changes in the technologies should be made. Based on current trends, one can expect that information technology developments will continue to fall in price in the future. Biotechnology, a younger technology, is likely to be more expensive in the shortmedium term, cheaper thereafter. Nanotechnology is likely to be very expensive in the medium term due to significant obstacles (e.g. absence of mass production techniques and physical barriers). The following sub-sections of the paper take a closer examination of these four industries and consider the likely effects of technological change in the pervasive technologies on the input structure and their subsequent implications for output, resource efficiency and, hence, greenhouse gas emissions to 2050. It is important to note that the impacts of technological change in all sectors were discussed without assuming any adaptation by the system to the effects of climate change. We assume that the current production of energy, generated predominantly by fossil fuels, will persist over the next 50 years. 3.1. Agricultural sector The agriculture sector is a typical Supplier Dominated General (SDG) sector. As shown in Table 1, firms in the SDG sector are likely to be found in the ‘traditional’ manufacturing and agriculture sectors, firms are generally small, have weak inhouse R&D capabilities and are most likely to source innovation from equipment and manufacturing suppliers, large clients and government funded research. Technology tends to be appropriated on the basis of professional skills, design, trademarks and advertising and technological trajectories are focused on cost cutting to meet the demands of the price-sensitive user. These industries can be described as

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predominantly mature and are just as likely to be located in developed as in less developed countries. The SDG sector as a whole and the agriculture sector in particular is more significant in terms of the proportion of total country output in China than in either the US or EU. Fig. 2 shows that the SDG sector output constitutes nearly 40% of total output in China but less than 20% in the US and EU. Disaggregated, the GTAP data shows that the agriculture industry accounts for 12% of total output in China compared to 2.5% in both the US and EU. The SDG sector is very important in terms of country/regional emissions as Fig. 2 shows, accounting for around 45% of total CO2 emissions in both China and Europe and 60% in the USA. Most of these emissions can be attributed to the energy production and energy extraction sectors; agriculture contributes 4% to total sector emissions in China, 1.5% in the USA and 7% in the EU. Agricultural CO2 emissions predominantly emanate from practices such as peat and fenland drainage, timber harvesting or conversion of grassland into arable land.6 As Table 2 shows, the agriculture sectors in the USA and EU are ‘cleaner’ than in China (in terms of resource efficiency, measured as emissions per unit output). Biotechnology is already beginning to transform the agriculture and food industry with the production of genetically modified plants and foods (though less so in Europe than in the USA). Developments in newer organism manipulation technologies, particularly those that draw on rapidly growing knowledge of plant and animal genomes, leading to new food products and new crop management methods might, in the timescale we are considering, win over currently sceptical public opinion and lead to increased production of more ‘biotechnology’-based foods. In terms of changes to the input structure in the EU, the use of genetically modified crops with improved properties (e.g. crops designed to grow faster in previously unproductive environments using the cacti gene to tolerate drought) are likely to be reflected in higher value circular inputs into the agriculture sector, stemming, for example, from the seed industry [36,45]. Circular inputs are currently the largest input into the agriculture sector accounting for 29% of total inputs into the EU agriculture industry (see Table 3). Biotechnology may also enhance the targeted application of pesticides and fertilisers (e.g. in vivo pesticides), reducing the volume of conventional fertiliser and pesticide inputs into the agriculture industry from the energy intensive chemical industry, which currently accounts for 8% of inputs into EU agriculture. However, since the price of new technologies are likely to be higher in the short–medium term than conventional fertilisers and pesticides, the net effect on value inputs is difficult to judge but will arguably be insignificant in the short-medium term, negative in the medium-long term. It may also be argued that biotechnology may facilitate improvements in the effective diagnosis and treatment of cattle disease (e.g. diagnostic tests and recombinant DNA vac6 However, agriculture greenhouse gas emissions are likely to be understated by simply looking at their CO2 emissions since the sector is a significant contributor of other greenhouse gases, notably methane and nitrous oxide, which stem from animal digestive processes, animal wastes and fertiliser use.

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cines for rinderpest, cowdriosis, theileriosis and foot and mouth disease) that will increase inputs from the veterinary sector [45].7 The health and veterinary sector currently provides between 1% and 2% of agriculture inputs in the EU. In output terms, it is likely that biotechnology will increase output from the agriculture industry in the EU more than emissions resulting in a net reduction in emissions per unit output. Future developments in information technology could lead to increases in the volume of inputs from the telecommunications industry, for example in the use of Global Satellite Positioning (GSP) systems to apply fertilisers accurately across a field [3]. Increases in the use of computer hardware, for example to monitor communications and sensor data would increase the volume of inputs from the office machines industry in the short–medium term. Also, with more computer hardware comes an increased need for sophisticated software (e.g. for data analysis) and support services. Increases in the volume of inputs of software and service commodities are likely to be reflected in higher inputs from the computer services sector.8 At present, the inputs of telecommunications, computer hardware and software/services account for less than 1% of inputs into the EU agriculture sector. As the price of information and communications technologies fall, the initial increase in the volume of information technology-related inputs will be offset and the value inputs will not significantly change. The increased use of information technology in the agriculture sector is likely to be more energy intensive but it could be argued that the better land management will be more resource efficient (e.g. more efficient in the use of resources, such as lime). Output is likely to increase as a result of these technologies so one could expect that emissions per unit output will fall over the next 50 years. It is unlikely that nanotechnology will have a significant impact on the agriculture sector before 2020. However, following 2020 it is likely (if nanotechnology meets the current expectations of facilitating molecular manipulation) that nanotechnologies will continue the path forged by information technology and biotechnology advances whilst providing new niche applications. These are likely to be high cost in the short–medium term (falling in the longer term, after 2020) but lead to significant improvements in the resource efficiency of agricultural output. For example, the use of remote nanosensors to monitor the efficient use of fertiliser applications and compact ultrasensitive chemical and biological sensors for food supply protection [19]. If one assumes that these developments will stem from the pharmaceuticals industry (a sub-sector of the chemicals industry) then one can attribute increasing inputs in value terms to the chemical industry. Also, future improvements in the processing speed, memory and computational capabilities of information technology software and hardware are likely to be facilitated by nano7 The veterinary sector is aggregated within the health services or health and education input-output sector. At a more aggregated level (e.g. in the E3MG data), one would find the veterinary sector in the non-market services sector. 8 The computer services sector is often aggregated within the ‘other market services’ input-output sector in the E3MG data.

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Table 3 Effect of technological change on inputs into the agriculture sector 1995 industry input Likely significant input changes into Generic techinto EU 15 Agriculture Agriculture as a result of technological nology driving sector change and diffusion change 11.3%

29.3% Between 1 and 2% 0.3% 0.02%

0.6%

Reduction in value of ‘bulk’ inputs from the chemicals sector; Increase in the value of pharmaceuticals (specialist) inputs from the chemicals sector Increase in value of circular (i.e. agricultural) inputs Increase in value of veterinary inputs (i.e. from the non-market sector) No significant change in value of communications inputs No significant change in value of computer hardware inputs (i.e. from the ‘office machines’ sector); Increases in the value of computer hardware inputs (i.e. from the ‘office machines’ sector) No significant change in value of computer services sector (i.e. from the other market services sector); Increase in value of computer services sector (i.e. from the other market services sector)

Pervasive effects occurring when?

Organism manipulation; Materials manipulation Organism manipulation Organism manipulation Information manipulation Information manipulation; Materials manipulation

Medium term (by 2020); Medium-long term (post 2020)

Information manipulation; Materials manipulation

Short-medium term (pre 2020); Mediumlong term (post 2020)

Medium term (by 2020) Medium term (by 2020) Short-medium term (pre 2020) Short-medium term (pre 2020); Mediumlong term (post 2020)

Source: 1995 input/output data from Cambridge Econometrics E3MG model.

technology e.g. quantum-switch-based computing (using quantum effects such as spin polarisation of electrons to determine the state of switches) or molecular computing (using synthesised organic compounds as logic switches and carbon nanotubes as interconnections) displacing conventional digital computers. This will increase the value of inputs from the computer services and office machines sector in the longer term. In summary, developments in the technologies that manipulate organisms, information and materials will affect the composition of the input structure into the agriculture industry. The likely significant effects are shown in Table 3. These changes are likely to be driven by developments in the manipulation of organisms and information in the short-medium term and by nanotechnology in the longer-term. As a result of these developments, output from the agriculture industry will increase significantly but this does not mean that growth in emissions per unit output will increase. In fact, on the contrary, it is likely that technologically-enabled resource efficiency gains in the agriculture production process will reduce average greenhouse gas emissions from the EU agriculture industry.

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3.2. Health services sector The health services sector is a key sub-sector of the Supplier Dominated Services (SDS) sector. SDS firms include providers of personal services (e.g. restaurants, hotels) and public/social services (e.g. health, education, defence and social services). As Table 1 shows, in general, firms in these sectors make only a minor contribution to their process technology; any innovations that they introduce are embedded in or stem from equipment, information and materials provided by manufacturing sectors. The technological trajectories of the two types of SDS firms are governed by the type of user (performance-sensitive for personal services and quality-sensitive for public and social services) and innovations are geared toward (service) product design and improving performance. Output and emissions are considerably smaller in the SDS sector than in the SDG sector, constituting just over 12% of European output and less than 5% of total region CO2 emissions in 1995 (see Figs. 1 and 2). The social services part of the SDS sector is dominated by health services. For example, in the UK in 1995, output from the health services industry purchased by all other industries was three times the output of education, six times the output of social services and ten times the output of public administration and defence.9 The output of the SDS sector is greatest in Europe (particularly Germany) and the USA and, in comparison with China, is relatively energy efficient. As Table 2 shows, emissions per output in China are nearly double that in the European Union.10 Emissions can be attributed to the delivery of health services, especially from hospitals, GP surgeries, government research laboratories, etc. This includes all energy-related costs incurred throughout the life cycle of buildings (e.g. maintenance, heating and lighting) and from the operation of capital housed inside the buildings (e.g. intensive-care machines, operating theatres). In terms of inputs into the health services sector, developments in organism manipulation are likely to increase the value of inputs from the pharmaceuticals sub-sector of the chemicals industry, which currently accounts for 5.7% of total inputs in the EU. For example, the use of genetic profiling to better diagnose disease and disease predispositions and the use of genetically engineered micro-organisms for disease resistance, vaccine and treatment (e.g. for AIDS, malaria, cancer and nervous disorders) [24,36,40,45]. Genetically engineered biosensors, which could also stem from the pharmaceuticals sub-sector are also likely to be employed for continuous health monitoring and laboratory analyses [36]. Outside the pharmaceuticals sector, there are also likely to be higher value inputs from the medical and precision implements sector (a sub-sector of the electricals sector) as a result of 9

When compared to private services, the health service’s output is comparable to that of hotels, pubs and catering. The data presented in Figs. 1 and 2 only includes the public/social services part of the SDS sector. 10 This relationship holds for other developed versus developing countries. For example, emissions levels in the former Soviet Union (FSU) and Northern Africa are equivalent to those in the USA, despite output in the former regions being significantly smaller.

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biotechnology-enabled surgical tools and techniques (e.g. angioplasty; laser perforations of heart tissue; and vaccines administered in a jet of gas, eliminating the need for syringes and refrigeration) [36,45]. Future developments in information and communication technologies are likely to increase the volume of inputs from those sectors deeply associated with information technology: the telecommunications sector, office machines and computer services sector. For example, one could imagine higher inputs from the communications sector reflected in more remote diagnosis via the internet and electronic health monitoring via mobile phones (e.g. monitoring heart rate, blood pressure, temperature and oxygen levels) [15]. Increased inputs from the office machines sector caused by higher ‘computer hardware’ inputs (e.g. computer hardware to provide infrastructure for remote diagnosis) and from the computer services sector and management consultancy sub-sectors (e.g. to provide the software for and to service the infrastructure). These volume increases are likely however to be offset by price reductions. Nanotechnologies are likely to affect inputs from the same industries in the longer term. Increased high value inputs from the pharmaceuticals sector; for example for advanced nano-engineered drug delivery technologies, enabling correct drug dosage to be delivered at the correct time to the target area can be envisaged [24]. Also, one could attribute increases in the application of nano-biomedicine sensors for monitoring and early disease detection to the pharmaceuticals industry (e.g. biocompatible sensors for in vivo early disease detection, imaging using new or improved contrast agents may be able to reveal tumours only a few cells in size and nanometer scale modifications of implant surfaces could improve durability and biocompatibility) [2,9,20]. Sensors built on nano-electromechanical systems (NEMS), using a millionth or billionth of the power of traditional transistors, offers the possibility of widespread diffusion of cheap, ultraminiature ‘smart’ sensors that could continuously monitor all the important functions in a hospital [39]. Advanced materials manipulation could create the nanodevices both to facilitate the creation of information technology-based self-diagnosis equipment and to help combat disease effectively. These developments, in input terms, advanced surgery techniques may arguably stem from the medical and precision instruments sector (a sub-sector of the electricals sector), advanced computer hardware from the office machines sector and support services/data analysis tools from the computer services (a sub-sector of the other market services sector). Table 4 summarises the above examples and shows how developments in technologies that manipulate organisms, information and materials could enhance resource management and resource efficiency in the health sector. Advances in the manipulation of organisms will arguably have the most significant impact on the public health services sector and will represent the most visible change to the public. New drugs, drug delivery technologies and surgical tools will increase output from the health sector, though the growth in output may not be as large as one might expect following the development of diagnosis technologies and we may see more emphasis on preventative rather than curative medicine. Information technologies will also facilitate the move toward preventative medicine with increased pub-

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Table 4 Effect of technological change on the inputs into the health services sector 1995 industry input into EU 15 NonMarket Services sector

Likely significant net input changes Generic techinto Health Services as a result of tech- nology driving nological change and diffusion change

Pervasive effects occurring when?

5.8%

Increase in the value of pharmaceutical Organism inputs from the chemicals sector manipulation; Materials manipulation Increase in value of medical and preOrganism cision implements inputs from the elec- manipulation; tricals sector; Increase in value of Materials medical and precision implements inputs manipulation from the electricals sector No significant change in value of com- Information munications inputs manipulation No significant change in value of com- Information puter hardware inputs (i.e. from the manipulation; ‘office machines’ sector); Increases in Materials the value of computer hardware inputs manipulation (i.e. from the ‘office machines’ sector) No significant change in value of Information computer services sector (i.e. from manipulation; the other market services sector); Materials Increase in value of computer manipulation services sector (i.e. from the other market services sector)

Medium term (by 2020); Medium-long term (post 2020)

2.7%

3.7% 1.3%

29.1%

Medium term (by 2020); Medium-long term (post 2020)

Short-medium term (pre 2020) Short-medium term (pre 2020); Mediumlong term (post 2020)

Short-medium term (pre 2020); Mediumlong term (post 2020)

Source: 1995 input/output data from Cambridge Econometrics E3MG model.

lic awareness of treatments through internet advice. Preventative healthcare is likely to be cheaper and more resource efficient in the longer-term. Advances in information technologies will improve administration, offering a small efficiency increase but they would also increase the volume of energy-using equipment. Developments in information technology could also increase resource efficiency by greater internet-based/virtual health care provision (e.g. virtual TV-based self-diagnosis technology) reducing demand for doctors and surgeries. Developments in the manipulation of materials are likely to improve the resource efficiency of the health services sector significantly, though this will not be before 2020. The resulting balance of the scale effects associated with the growth in output (and hence energy consumption) on the one hand and the expected improvements in energy efficiency on the other, is not clear. Manipulations of information, organisms and materials are likely to increase output whilst also contributing toward lower resource intensity. 3.3. (Bulk) chemicals sector The chemicals industry one of the largest in a group of industries belonging to the Scale Intensive General (SIG) sector. Also in the sector are other industries

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that provide bulk or assembly products such as steel and motor vehicles. As Table 1 shows, important sources of process improvements come from the production engineering departments and from small-specialised external companies that supply the specialised machinery (see Section 3.4). In-house engineering departments focus on improving the performance of complex integrated production processes—identifying and correcting imbalances and bottlenecks to improve productivity. Technological advances are reflected in the efficient continuous running of large-scale integrated production processes to produce a final good and are protected through in-house know-how, secrecy and patents. In absolute terms, the SIG sector is the largest of our sectors in terms of global output and the second largest in terms of emissions (behind the SDG sector) (GTAP, 1995). In the three countries/regions shown in Figs. 1 and 2, the SIG sector contributes between 22% (in the USA) and 30% (in China) of total output. Emissions from the sector vary between 17% (in the USA) and 35% (in China) of total emissions. In absolute terms, the output and emissions pattern is familiar. Globally, production from the SIG industries is concentrated in the developed countries and in Europe (Germany in particular), the USA and Japan. This pattern also holds at the disaggregated chemical industry level. However, emissions in these regions are relatively low, as Table 2 shows for the EU and USA. The largest polluters in the chemical industry relative to their output are producers in Central Europe and the Former Soviet Union and Asia (and China in particular as shown in Table 2).11 Chemical industry production covers the spectrum from low value-added bulk chemicals to high value-added low volume chemicals; from organic chemicals to dyes and paints and highly sophisticated speciality products for the pharmaceutical and agrochemicals industry [41]. The large chemicals firms provide products across this spectrum, arguably operating in two different markets: a concentrated organic chemicals mature market and a fiercely competitive specialised chemicals growth market. This section analyses the effect of technological change on the bulk production process (for technological change will affect the specialist pharmaceutical sub-sector of the chemicals industry, a Science Based General (SBG) sector in a different way (see Table 1)).12 The largest inputs in volume terms into the chemicals industry in the EU are circular inputs, accounting for over 40%. Likely reductions in the volume of circular inputs caused by displacement of conventional chemical production methods will probably be more than offset by a rise in (higher priced) inputs of biotechnologyenhanced production processes (e.g. the displacement of metal or organic solvents/ inorganic acid catalysts by enzyme or genetically modified catalysts) [32]. In value 11

For instance, although emissions of CO2 are similar in China and Japan (both produced 0.142bn tonnes of CO2 emissions in 1995), output in Japan was nearly five times greater than in China (US$52.57bn and US$10.96 respectively). 12 The analysis of the way in which technological change in the information technology, biotechnology and nanotechnology technologies affects the pharmaceuticals industry (and the SBG sector in general) has been undertaken and though not presented here is available directly from the authors.

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terms, with rising prices to the medium term we are likely to see increased circular inputs. Advanced biotechnology-facilitated production processes are also likely to significantly reduce value inputs of electricity (currently 5.2% of total inputs into the EU chemicals industry) (e.g. use of an enzyme-catalysed polymerisation process in the production of polymers) [32] and inputs of refined oil inputs (currently 5.8% of total inputs) (e.g. biotechnology catalysts using plant-derived feedstocks instead of petroleum-based feedstocks in the production of chemicals and synthetic fibres) [14]. Developments in the manipulation of information in the short-medium term is likely to lead to increased volume of inputs from the office machines sector as a result of the widespread application of computers in the production process. Increases in the office machine sector are likely to be mirrored by increases in the volume of inputs from the computer services sub-sector of the other market services sector. With falling information technology prices, value inputs are unlikely to rise significantly in the short-term. However, value inputs from each of these sectors is likely to increase in the longer-term with the pervasive diffusion of nanoTable 5 Effect of technological change on the inputs into the chemicals sector 1995 industry input Likely significant net input changes Generic techinto the EU 15 Chemi- into the EU chemical industry as a nology driving cals sector result of technological change and dif- change fusion

Pervasive effects occurring when?

42.9%

Organism manipulation Organism manipulation Organism manipulation Information manipulation; Materials manipulation

Medium term (by 2020) Medium term (by 2020) Medium term (by 2020) Short-medium term (pre 2020); Mediumlong term (post 2020)

Information manipulation; Materials manipulation

Short-medium term (pre 2020); Mediumlong term (post 2020)

Materials manipulation

Medium-long term (post 2020)

Materials manipulation

Medium-long term (post 2020)

5.2% 5.8% 0.11%

12.4%

0.37%

0.11%

Increase in value of circular inputs from the chemicals sector Reduction in value of inputs from the electricity sector Reduction in value of inputs from the refined oil industry No significant change in value of computer hardware inputs (i.e. from the ‘office machines’ sector); Increases in the value of computer hardware inputs (i.e. from the ‘office machines’ sector) No significant change in value of computer services sector (i.e. from the other market services sector); Increase in value of computer services sector (i.e. from the other market services sector) Increase in the value of inputs from the medical and precision implements subsector of the electrical sector Increase in the value of inputs from the electronics sub-sector of the office machines sector

Source: 1995 input/output data from Cambridge Econometrics E3MG model.

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technologies (e.g. faster, more powerful computers with higher computational power and more advanced software for molecular modelling) [9]. Nanotechnologies may also increase the value of inputs from the medical and precision implements sub-sector of the electrical sector to control the production of chemical products at the molecular level. We are also likely to see increases the volume of inputs from the electronics components sector (a sub-sector of the office machines sector) through the use of nano-sensors (e.g. a network of sensors to monitor emissions and environmental impacts across a plant, centrally co-ordinated) [9] and nanotechnology enabled integration of chemical and electrical logic and memory components (e.g. applications for chemical synthesis) [36,46]. Table 5 summarises the effects of technological change in the information-, bioand nano-technologies, on the input structure of the ‘bulk’ chemicals industry. In summary, the effects of technological change, particularly in the manipulation of organisms, will increase output of SIG industries. However, advanced materials and information technologies will increase resource intensity and overall, in the EU there is likely to be a net effect of reducing greenhouse gas emissions. For instance, developments in the manipulation of information (e.g. improved sensing devices) should result in better monitoring of industrial processes, allowing firms to eliminate waste and use capacity more efficiently [22]. Also, nano-machines may provide valuable tools for chemical synthesis and sensing devices [46]. 3.4. Agriculture and industrial machinery industry The agriculture and industrial machinery industry is an example of the Specialised Supplier General (SSG) sector. As Table 1 shows, the SSG sector includes firms that, despite producing a relatively large proportion of their own process innovation, are a major source of innovation for other larger firms, including, for example, those in the SIG sector, complementing innovations sourced from their own engineering departments. These firms include mechanical and instrument engineering firms, such as those in machinery production and transport equipment production. Small firms, with specialised knowledge and experience, supply the equipment and instrumentation to the SIG firms and because the cost of poor performance is high, technological trajectories are geared toward providing high-quality performance-increasing product innovation. In contrast to the production engineering departments of SIG firms, external competitive pressures ensures that the appropriation of technology depends on providing a continually improving, reliable product that meets the users’ requirements. As Figs. 1 and 2 show output in the SSG is small, less than one third of the output from either the SDG or SIG sectors in any of the three regions/countries. Although the proportion of industry in each country/region accounted for by the SSG sector is similar, in absolute terms, output in the EU and US far exceeds that in China. In fact, when examining global data one finds that 75% of output from firms in the SSG sector in 1995 are concentrated in the TRIAD (the EU, USA and Japan). Emissions predominantly stem from the production of specialised machinery and instrumentation for external users and SSG firms in these three global

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regions are not a significant source of CO2 emissions. As a proportion of total emissions, SSG firms contribute between 1.6% in the US and 4% in China. However, as Table 2 shows, output is considerably more dirty in China than in the US or EU. What is more, there appears to be a developed/less developed country divide as the trend is maintained across global data. For instance, SSG firms in Central Europe and (in particular) the former Soviet Union produce 14 times less output than the EU but produce more emissions.13 As one would perhaps expect, the largest inputs into the agriculture and industrial machinery industry are circular (23% of total inputs in the EU) and from the metal products industry (17.4% of total inputs). A significant proportion of total inputs also stems from the distribution (8.4% of total inputs into the EU) and other market services (14.6% of total inputs) sectors are also large providers of inputs. For firms in the agriculture and industrial machinery industry (and in the wider SSG sector), improvements in the product design are likely to be aimed at improving quality and the manipulation of information and materials are likely to have an important impact on product design. Biotechnology developments may affect the value of inputs from the pharmaceuticals and other subsectors of the chemical industry (currently worth 1.4% of total inputs in the EU), reflected for instance in the greater use of bio-engineered products in the production process (e.g. use of catalysts in the manufacturing process) and in end products [9,20]. Developments in information technology are likely to affect inputs from the computer hardware and electronic components (sub-sectors of the office machines sector), currently accounting for 0.8% of total inputs in the EU. Although inputs from this sector are currently low, the widespread application of automation and robotics and greater use of ‘electrical components’ with the pervasion of more compact, faster chips using DNA crystals will significantly increase their inputs [47]. Developments in software and computer support services will need to manage the increased computer hardware and we are therefore likely to see increases in the inputs into the agriculture and industrial machinery industry from the computer services sub-sector of the other market services sector. As before, these volume increases are unlikely to translate into value increases beyond the short-term with falling prices of information technology-enabled products and components. In the longer term, developments in the manipulation of materials will have pervasive effect on the agriculture and industrial machinery sector, which will be reflected in changes in the input structure. For example, higher value inputs are likely to stem from the medical and precision implements sub-sector of the electricals sector (e.g. scanning probe microscopy) for nano-engineering machinery (e.g. new instruments to manipulate atoms, and small particles for miniaturisation) [20]. Also, inputs in value terms are likely to be higher from the electronic 13 In 1995, output from the SSG sector in the EU was US$114.3bn contributing 0.0596bn tonnes of CO2 emissions. In Central Europe and the former Soviet Union output totalled US$8.26 with emissions of 0.062bn tonnes of CO2 emissions.

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components sector (also in the electricals sector) in the form of smaller, more efficient and lower cost nano-motors, for example using shape memory alloys or carbon nanotubes [15,16,20] and from the computer hardware sub-sector of the office machines sector and from the computer services sub-sector of the other market services sector (e.g. providing the necessary hardware and software for advanced molecular modelling). In addition, higher value inputs from the optics sub-sector of the telecommunications sector may be expected to (e.g. photonic bandgap fibres for use in lasers to cut through metals) [44]. As referred to above, the largest input into the EU agriculture and industrial machinery industry are from the metal products industry and these are likely to decrease following future developments in materials technology. For example, advanced materials (e.g. carbon nanotubes) may displace the use of iron, steel and other ferrous metals significantly reducing inputs from the metal products sector. This would lead to an increase in the value of inputs from an as yet unspecified sector, which for the sake of this analysis we will call the ‘advanced material

Table 6 Effect of technological change on the inputs into the agriculture and industrial machinery sector 1995 industry input into Likely significant net input changes Generic techthe EU 15 agriculture into the EU 15 agriculture and indus- nology driving and industry trial machinery sector as a result of change technological change and diffusion

Pervasive effects occurring when?

1.4%

Organism manipulation Information manipulation; Materials manipulation

Medium term (by 2020) Short-medium term (pre 2020); Mediumlong term (post 2020)

Information manipulation; Materials manipulation

Short-medium term (pre 2020); Mediumlong term (post 2020)

Materials manipulation

Medium-long term (post 2020)

Materials manipulation Materials manipulation Materials manipulation

Medium-long term (post 2020) Medium-long term (post 2020) Medium-long term (post 2020)

0.8%

14.6%

7.3%

17.4% 1.4% –

Increased value of pharmaceuticals inputs from the chemicals sector No significant change in value of computer hardware inputs (i.e. from the ‘office machines’ sector); Increases in the value of computer hardware inputs (i.e. from the ‘office machines’ sector) No significant change in value of computer services sector (i.e. from the other market services sector); Increase in value of computer services sector (i.e. from the other market services sector) Increase in value of medical and precision implements inputs from the electricals sector Reduction in the value of inputs from the metal products sector Increase in the value of optics inputs from the telecommunications sector Increase in the value of inputs from the ‘advanced material products’ sector

Source: 1995 input/output data from Cambridge Econometrics E3MG model.

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products’ sector. This sector would have the capacity to mass produce advanced materials for inputs into other sectors. A new materials sector built on nanoscience would have many applications for industry. Nanomaterials (e.g. carbon based composite structural materials combining strength, light-weight, electrical and thermal conductivity) could displace conventional metal products; nanoparticulates (e.g. dry lubricant) could be used for improving the engineering process; and harder, cheaper compounds developed at the nanoscale (e.g. the BMA compound replacing cubic-boron nitride) could be used to improve the properties of cutting and grinding tools [17,20,37,40]. Table 6 summarises the effects of technological change in the pervasive technologies on the input structure and resource efficiency of the agricultural and industrial machinery industry. One can expect the output of the agriculture and industrial machinery industry to increase quite significantly over the next 50 years as a result of developments in organisms, information and materials manipulation. Since other sectors rely on the outputs from this sector as an important source of innovation, one could argue that although the resource efficiency is not likely to increase in this industry, developments in this industry will lead to significant reductions in downstream industries in the SDG and SIG sector. Information and communications technology will have a pervasive effect on the agriculture and industrial machinery industry and other SSG industries in the short–medium term, particularly in improving the efficiency of the production process. These developments are likely to be more energy intensive, though whether resource savings elsewhere will counter balance this effect is unclear. As a major user of output from the metals industry, the long-term future of the agriculture and industrial machinery industry and other SSG industries is likely to be considerably affected by developments in the manipulation of materials.

4. Implications and conclusions This paper describes a method to consider the long-term effects of technological change on industrial structure and energy demand. As such, it seeks to integrate insights from studies of technology regarding long-term growth with questions of sustainability. Using a methodology based on long-wave theory and an industrial classification based on technological characteristics, the likely effects of pervasive technologies on the input-output structure of sectors representing four out of the eight aggregated technological categories. Implications for the industry emission levels of industrial greenhouse gases are considered. The analysis shows that changes in technologies that manipulate information, organisms and materials will have a significant effect on the future industrial structure. Using value changes in the input composition, we show that this effect is dependent on the long-term diffusion of these technologies through an industry, itself a function of time. The qualitative analysis also demonstrates that technological change will affect different industries in different ways and at different speeds. By taking an example of a sector from four of our eight broad technological

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categories and using emissions and input output data for the EU we described qualitatively the effects of technologically induced change in the industrial structure on future energy demand and greenhouse gas emissions. The two most significant sectors in terms of greenhouse gas emissions currently—the Supplier Dominated General sector (SDG) and the Scale Intensive General (SIG) sector—are both likely to see increased sectoral output as a result of developments in the manipulation of organisms, information and materials. Nevertheless, future organism, information and materials technologies will also increase the resource efficiency of these sectors, probably by a higher magnitude, the net effect of which will be a reduction in greenhouse gas emissions. Similarly, the trade-off between higher output and lower resource intensity, as a result of developments in organism, information and materials manipulation will result in lower greenhouse gas emissions in the EU in the Supplier Dominated Service sector. The application of information and materials technologies by firms in the Specialised Supplier General sector, a sector which is the source of machinery and components for all other sectors, is particularly important in increasing the resource efficiency of production processes of firms in the SDG and SIG sectors. For firms within the Specialised Supplier General sector though, increased emissions are likely to stem from increased output resulting in lower resource intensity and a net effect of slightly increased greenhouse gas emissions. These developments are location specific. The analysis has shown that different sectors have different levels of output and emissions in China than in the EU and USA. The input-output structure of the same industries in different countries is also different. Also, the international division of labour in the different sectors is shifting between countries. Countries have different institutional factors that facilitate or hinder the diffusion of new technologies through different sectors. The role of multinationals in creating global changes in economic activity as well as a new international division of labour are also important in determining different country specialisation and diffusion of technology and consequent environmental effects. We are currently working on the locational aspects of technological change [26]. The method we have described and the (provisional) results we have reported are qualitative. We are also developing an extension of the method that will try to quantify the effects of technological change using an online Delphi study. Our paper describes a methodology by which one can consider the long-term effects of technological change on industrial structure and energy demand. The qualitative findings suggest that industrial policy addressing the development and diffusion of pervasive technologies may have an important role in forging a path of economic growth that can meet the challenge of sustainability from an ecological point of view. The scope of these changes will vary in different countries, but they will have in common, both at national and at firm level, the assimilation and effective use of information technology, biotechnology and nanotechnology.

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Acknowledgements We gratefully acknowledge research support received from the Tyndall Centre for Climate Change Research. We are also grateful for the participation of Rod Coombs, Clair Gough, Ian Miles, Simon Shackley and Vivien Walsh at the workshop held at the Manchester School of Management, UMIST and for comments on early drafts by Jonathan Kohler, Haoran Pan and Frans Berkhout.

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