In the summer of 2026, Europe is breaking records: weeks-long heatwaves, persistent droughts, and large-scale wildfires of unprecedented proportions dominate the headlines. Other regions of the world are faring no better. Climate change and its economic and social impacts are no longer a distant forecast but a new—if expected—reality that demands concrete solutions and presents opportunities.

These are opportunities Austria should not squander, especially as it is—compared to many other countries—actually well-equipped to seize them. Over the past decades, Austria, which has long seen itself as a pioneer in environmental matters, has earned a global reputation as a green technology leader. It has successfully exported its innovative energy and environmental technologies worldwide, making a significant contribution to competitiveness and prosperity.

We are neglecting our proven strengths

Austria holds an excellent position in a market that, despite scepticism from the US and others, is growing rapidly worldwide. Economic projections estimate that the green technology market—depending on the definition—will grow from approximately USD 33 billion today to USD 211 billion by 2034. For clean energy, analysts expect growth from the current USD 1.3 trillion to USD 3.3 trillion over the same period. This comes as no surprise: every country is affected by the impacts of climate change, and each is striving to adapt to rapid changes by developing or procuring climate-resilient infrastructure and technologies.

Yet it is precisely in this field that Austria is neglecting its proven strengths: as the STI Monitor 2026 starkly illustrates, R&D spending on environmental and climate initiatives has declined significantly, both in relative and absolute terms—from an average of 75.6% of the spending by Innovation Leaders between 2015 and 2024 to just 50% today. For comparison: leading nations such as Japan, Finland, and South Korea spend more than double what the Innovation Leaders invest in environmental and climate R&D. This has several adverse consequences for Austria.

1. We are squandering our strengths.

Austria starts from a strong position: It is globally recognised as a green technology leader, with domestic environmental and energy technologies having been top exports for decades. Austria enjoys a strong reputation as a partner, particularly in manufacturing such systems. However, maintaining this status is not a given—it requires continuously offering new solutions and innovative technologies. A necessary transformation of existing business models is also on the horizon, with digitalisation, automation, and AI playing key roles. Incremental improvements are no longer enough.

2. We are neglecting global trends.

Climate change and geopolitics are driving global demand for smart, clean, and climate-compatible technologies—primarily in energy, but also in wastewater and exhaust gas treatment, urban technologies and systems, and the automotive sector. Austria should not leave this rapidly growing market to others. This is not just about climate protection but also about improving efficiency, reducing costs, and modernising—all of which benefit domestic industry.

3. We are neglecting our own energy security.

In energy supply, Austria lurches from one costly crisis to the next. We must become less dependent on energy imports—not for moral reasons, but out of pure self-interest. The scientific expertise and the companies are still here—for now. New challenges keep arising. Yet Austria is a pioneer in systemic solutions, and it should leverage this to gain greater sovereignty.

4. We are ignoring existing knowledge.

Despite increasingly long summer heatwaves, we are neglecting or even denying the need for rapid climate adaptation. This means developing solutions in collaboration with others and building partnerships with countries from which we can learn. It also means adapting our own infrastructure and settlements to new temperature regimes, prioritising water management, and developing climate-resilient technologies. The uncomfortable truth is that these transformations require long lead times, as they involve profound changes across many areas of life and the economy. This makes swift action all the more urgent.

5. We are not procuring with enough focus on innovation.

With an annual procurement volume of around €70 billion, the public sector is Austria’s largest buyer. This market power has the potential to drive innovation, translate research into market-ready solutions, and serve as an anchor customer to enable scaling at domestic sites—a key lever for overcoming Austria’s innovation paradox. However, as noted in the European Commission’s report on public procurement in Austria, insufficient attention is being paid to innovation-driven procurement in this country.

On a positive note, the federal government has identified environmental and energy technologies as one of nine key technology fields in its Austria 2035 Industrial Strategy and plans to address these areas in the announced Action Plan on Strategic Public Procurement. This suggests that research and development in environmental and energy technologies will be intensified in the future, strengthening Austria’s competitive edge. As previously mentioned: the sooner we begin this innovation-driven transformation, the lower the expected damage will be.

Leveraging Austria’s reputation as a sustainability frontrunner

The adverse effects outlined above are not inevitable. Above all, it is in Austria’s own hands to take action and make targeted investments. We have the knowledge, the companies, and the infrastructure to assume a leading role in energy and environmental technologies. What must not be overlooked is that innovative pilot projects and procurement deliver multiple benefits: they strengthen and showcase domestic innovation, benefit new and existing businesses, and ultimately help mitigate the impacts of climate change.

Austria alone cannot stop further climate change. However, by increasingly leveraging our technological expertise to prepare Austria as best as possible for the impacts of this new reality, we not only set an example but also capitalise on our reputation as a green technology leader to sell our environmental and energy innovations on the global market. In doing so, we also contribute to the global climate transformation beyond our borders.

At the beginning of March, the Office of the Productivity Board published, together with co-authors, a short analysis¹ of the “Industriestrategie Österreich 2035”². Its central message is that the strategy is an important step, but in its current form still too programmatic and not yet ready for implementation. The short analysis therefore acknowledges the strategic direction but criticises the lack of clear operationalisation, responsibilities, financing logic and a robust governance architecture. In the authors’ view, precisely these elements need to be added if a political framework document is to become an effective instrument of industrial policy.

What this can mean in concrete implementation terms is illustrated in this article using historical and current evidence: historical evidence is provided by a publication by Hartog et al.³, current evidence by Lin, Frey and Wu⁴.

Historical evidence as a lens on structural change in innovation

Why is it useful to look more closely at the publication by Hartog et al. in connection with the short analysis by the Office of the Productivity Council, even though the historical data covers the period from 1856 to 1945? If one wants to make structural change in the organisation of innovation empirically visible, one needs an observation period in which different historical data sources can be reliably linked with one another. Hartog et al. show that this is possible in the US case precisely because patent, census and laboratory data overlap in this period. This makes it possible to demonstrate empirically when, and under which organisational conditions, the US innovation system underwent a lasting structural transformation. The fact that organisational questions are not only historically relevant is also underlined by the current evidence in the paper by Lin, Frey and Wu. The authors analyse a total of 20 million scientific articles and 4 million patent applications over a period of around half a century.

Key statements and findings of the short analysis

The “Industriestrategie Österreich 2035” sets the right goals, but is not yet a robust implementation plan. For the first time, Austria has presented a cross-ministerial industrial strategy with six strategic goals, 117 measures and nine key technology fields. The main criticism in the short analysis is that many measures have not yet been designed as clearly steerable interventions with roadmaps, responsibilities and a coherent budgeting logic.

According to Hartog et al., the transformation towards an innovation-strong economic system does not succeed through abstract priority setting alone, but above all where new technological search processes are supported organisationally – by teams, firms and research laboratories. The current evidence from Lin, Frey and Wu complements this finding with an important contemporary dimension: even in a digitally networked knowledge economy, the way teams collaborate, and whether they can draw on physical proximity, shared infrastructures and direct interaction, remains relevant. This makes organisational questions even more pressing for the Austrian industrial strategy.

The core problem of Austrian industry is not clearly enough spelt out. The short analysis emphasises that Austria’s traditional industrial specialisation and its export-driven growth model are under pressure – a development that is also clearly visible in the FORWIT FTI Monitor since 2022. What is needed, according to the short analysis, is therefore not only adjustment but an accelerated structural shift towards new growth fields.

Hartog et al. do not directly prove this macroeconomic finding, but they do provide an important contribution to the question under which conditions such structural change can succeed from an innovation perspective: historical evidence from the US shows that the transition to a new innovation regime was heavily dependent on organisational carriers such as teams, engineers and industrial research laboratories, and on how this knowledge was brought together. Consequently, a shift into new, more complex growth fields requires not only technology but also appropriate institutional and organisational carriers. Lin, Frey and Wu reach a similar conclusion. The authors show that geographically dispersed teams produce breakthrough-oriented ideas less frequently on average than teams working together on site, because early conceptual work that relies heavily on tacit knowledge is more difficult to integrate digitally. This implies that the transition into new growth fields requires suitable innovation spaces.

The focus on key technologies is still too imprecise. The strategy identifies nine key technologies and provides for a budget of around €2.6 billion up to 2029. According to the short analysis, however, it remains unclear whether this in fact represents a new prioritisation, as a large share of the funds apparently is already committed in these areas. What is therefore needed is not merely a list of strategic technology fields, but a more precise delineation of areas of strength and funding priorities within these technologies.

Yet even if technology priorities are chosen sensibly in political terms, this still does not answer the question of how new industrial dynamics, market-ready innovation and scalable value creation are actually to emerge in these fields. Historically, Hartog et al. show that technological breakthroughs gained particular momentum where they were tied to robust organisational forms – such as permanent R&D structures, close linkages between science and industry, capable teams and systematically developed transfer capacities. Lin, Frey and Wu become even more concrete: key technologies need not only funding budgets, but above all physical and organisational carriers as translators – such as innovation hubs, clusters, shared lab spaces, pilot plants, testbeds and other application-oriented research infrastructures. It is often at these interfaces that the decision is made as to whether technological potential becomes marketable and scalable innovation. This means that if genuine areas of strength are to be defined within the nine key technologies, it must also be specified through which innovation ecosystems they are to be realised. The industrial strategy already refers to such elements; the additional insight from Hartog et al. and Lin, Frey and Wu is to understand these systems not as merely accompanying measures, but as central carriers of an effective key technology policy.

Governance is a weak point. The short analysis makes it clear that the strategy remains vague on who is politically, administratively and operationally responsible for which measures, how ministries are to cooperate and how the federal government, Länder, municipalities and the EU are to be involved. What is therefore needed is a genuine “whole-of-government” approach with clear roles, responsibilities and escalation mechanisms.

Hartog et al. also provide strong argumentative support for this observation, as they describe organisational innovations themselves as drivers of technological progress. Governance is therefore not just a matter of administrative detail, but an integral part of the innovation strategy: if teams, research, transfer and industrial application are not institutionally organised, the innovation impact of the strategy is weakened. The findings of Lin, Frey and Wu reinforce this argument: if spatial proximity, co-presence and the quality of collaborative settings remain relevant for disruptive innovation, then governance and strategy must also define where and how such innovation spaces are created, operated and interconnected.

Many measures are still insufficiently concrete and the financing logic remains unclear. According to the short analysis, around 60% of the measures contain, at least in part, declarations of intent or problem descriptions rather than clearly operationalisable interventions; for more than half, it remains open whether additional funds are needed and how they are to be financed.

As Hartog et al. show, industrial research was historically successful above all because of long-term planning horizons, stable resources and a close link to concrete industrial missions. This supports the short analysis’s call for multi-annual budget paths, clear prioritisation and a robust financing architecture. The current evidence from Lin, Frey and Wu on remote versus on-site collaboration also suggests that investments in shared infrastructures, pilot environments and physical innovation spaces should not be treated as a side issue, but as productivity-relevant components of an innovation-oriented industrial policy.

KPI monitoring is not enough. A central point in the short analysis is that macroeconomic indicators alone do not provide a robust basis for assessing the effectiveness of individual measures. This requires explicit impact logics, process goals, milestones, evaluations and in-depth analyses.

Hartog et al. are even more specific here: innovation systems change through teamwork, skills profiles, links to science, spatial concentration and participation. Lin, Frey and Wu expand this perspective by adding the quality of collaboration itself. Good monitoring should therefore capture not only high-level KPIs, but also cooperation structures, transfer activities, skills profiles, regional concentration and inclusion – and, in addition, track whether physical innovation infrastructures are actually used, the extent to which research and firms work together in co-presence, and where co-location or shared research environments contribute to the emergence of new ideas.

The recommended solution is field-specific roadmaps. The authors of the short analysis propose translating the respective fields of action in the industrial strategy into roadmaps that systematically link objectives, measures, resources, responsibilities, timelines and milestones.

On the basis of their historical analysis, Hartog et al. further suggest that roadmaps should not only list technologies or funding instruments, but also identify the organisational carriers of innovation: where will application-oriented R&D capacities emerge? Who coordinates science–industry linkages? How are teams, engineering capabilities and repeated collaboration built up? Lin, Frey and Wu suggest taking this roadmapping approach even further: roadmaps should specify which physical innovation spaces in each field are to be built up or strengthened and used, and which role they play in transferring research results into industrial application.

Monitoring, evaluation and policy learning should be set up as a learning system. The industrial strategy envisages that the Productivity Council will take on the monitoring function and that specialised research institutions will carry out evaluations. The short analysis proposes ex-ante impact assessments, accompanying evaluations, ex-post, sectoral and regional analyses, as well as the use of microdata sets, patent and project information and horizon scanning.

Hartog et al. also underline the value of evidence-based steering. They show that the innovation impact of particular organisational forms can change over time – for example, firm-based team research lost some of its radical novelty dynamics after 1950. This is precisely why a learning, adaptive steering system is more appropriate than a static instrument logic.

It is also important to link the industrial strategy with higher education and skills policy. Hartog et al. demonstrate that the rise of industrial research was historically closely linked to higher education, the emergence of engineers, increasing engagement with science and new forms of university involvement. This supports the short analysis’s conclusion that a technology strategy without a coordinated higher education, transfer and skills policy remains incomplete.

Start-ups, scale-ups and spin-offs

The “Industriestrategie Österreich 2035” refers in several places to start-ups, scale-ups and spin-offs. This is particularly explicit in the field of action “Research, Technology & Innovation”, where the “scaling of start-ups into future industrial companies” is highlighted, and where start-ups, scale-ups and spin-offs are explicitly described as important drivers of technology transfer and as potential industrial enterprises of tomorrow. In addition, performance agreements with universities are to place greater emphasis on technology transfer and spin-off measures, as well as entrepreneurship education. Furthermore, support and advisory services for business creation and scaling – such as spin-off fellowships, pre-seed and seed financing, and shared lab spaces – are mentioned.

In the short analysis, the topic is not developed as a separate priority and is only addressed indirectly. Nevertheless, it is criticised that the industrial strategy does not systematically examine whether the existing RTI funding system produces a “robust pipeline of young, high-growth firms” and thus supports the emergence of future flagship companies. The topic remains scattered across various fields of action without a clearly defined, integrated logic for transfer and scaling.

This is precisely where there is an important point of connection for start-ups and spin-offs. Lin, Frey and Wu argue that early, conceptual phases of innovation benefit particularly strongly from spatial proximity and direct collaboration. Shared lab spaces, clusters, pilot environments and application-oriented innovation ecosystems are therefore not a marginal issue for research-intensive start-ups and spin-offs, but a core element of a functioning transfer architecture. Their role as carriers of the translation of research results into value creation and markets should therefore be anchored explicitly, systematically and with a dedicated impact logic.

Conclusion

The overall direction of the industrial strategy is right, but without a precise impact logic, clear responsibilities, multi-annual budget paths, roadmaps and robust monitoring, it remains too weak in practice. Hartog et al. show historically that technological dynamism was particularly strong where teams, engineers and industrial research laboratories were brought together organisationally. Lin, Frey and Wu confirm this historical perspective with current evidence: even today, spatial proximity remains relevant for breakthrough-oriented innovation.

For Austria, this leads to a clear guiding principle: industrial policy cannot stop at naming key technologies and allocating funding. It must also actively shape the organisational and physical conditions for innovation – that is, governance, learning roadmaps, application-oriented R&D structures, clusters, shared lab spaces, pilot and test infrastructures, as well as a coherent transfer and scaling logic for start-ups, scale-ups and spin-offs. Only then can the “Industriestrategie Österreich 2035” evolve from a programmatic guideline into an effective instrument of industrial renewal.

1. Office of the Productivity Board and co-authors, Industriestrategie Österreich 2035: Von der programmatischen Leitlinie zur wirksamen Umsetzungs- und Steuerungsarchitektur↩
2. BMWET, Industriestrategie Österreich 2035 – für einen wettbewerbsfähigen Industriestandort und wirtschaftliche Resilienz.↩
3. Matte Hartog, Andres Gomez-Lievano, Ricardo Hausmann, Frank Neffke: „Inventing modern invention: The professionalization of technological progress in the US“, Research Policy, Elsevier, Volume 55, Issue 3, 2026↩
4. Lin, Y., Frey, C.B. & Wu, L.: „Remote collaboration fuses fewer breakthrough ideas“, Nature 623, 987–991, 2023↩

In previous blog posts analysing higher education, we have outlined the differences between Austrian higher education institutions across the four legally defined sectors. We now want to – as previously announced – take a new perspective and focus on the functional differentiation of higher education institutions.

By functional differentiation we mean the actual division of tasks and thematic specialisation of individual institutions within the broader system. The various sectors naturally play a key role here, since the relevant legal texts define their respective mandates and expectations. It is hardly surprising that academic teaching is a task assigned to institutions in all sectors. Research is explicitly mentioned in the laws governing public universities and university colleges of teacher education, with the latter required to ensure occupational relevance. Universities of applied sciences, in turn, are expected to address research mainly through methodological diversity and integrity within study and teaching activities. Interestingly, all four legislative frameworks stipulate cooperation as part of their remit.

Within each sector, however, we can identify even deeper features of functional differentiation. In the case of universities of applied sciences, for example, some offer a broad range of programmes, while others are more narrowly specialised – often reflecting their regional context, where they align with local industrial priorities or address specific training needs (such as in nursing). The university colleges of teacher education – which themselves respond to a highly specific training need, namely for Austria’s school teaching staff – fall into three types: nine general colleges (one per federal province), four theological ones (one per teacher-training network), and one dedicated to agricultural and environmental education.

Among private universities, we find considerable heterogeneity in thematic orientation. Broadly speaking, three main specialisation areas emerge: first, medical and life sciences; second, social sciences and humanities; and third, art and music. This thematic orientation is most easily understood if private universities are seen as agile vehicles intended to complement and compensate for areas of provision particularly in teaching, as offered by public universities.

That leaves the public universities, which – with 23 institutions – constitute by far the largest sector. Their internal functional differentiation is therefore not only the most pronounced but also the most significant for the entire system. It quickly becomes evident that these universities can be clustered by thematic orientation. Alongside six universities covering the full breadth of academic disciplines, there are four technical universities and three medical universities, representing two types with particular societal relevance. In addition, six universities of the arts reflect the high socio-political importance attributed to the “development and advancement of the arts” (as stated in the law), especially in Austria. Finally, there are four other universities, each with a distinct specialisation.

When grouping the public universities according to these categories and comparing them by two structural indicators – number of students and number of academic staff – it becomes clear that functional differentiation is also reflected in organisational characteristics. The general universities are, on average, significantly larger in both respects (1,813.5 full-time equivalent academic staff and 32,972 students). The technical and medical universities are comparable in terms of academic staff (TUs: 1,722.5 FTE; MUs: 1,778.1 FTE), but the latter have a much smaller student population (TUs: 15,523; MUs: 6,131). The other specialised universities combined (average: 758.3 FTE academic staff and 10,683 students) and the universities of the arts (average: 328.1 FTE and 2,189 students) are smaller still.

Even from this admittedly preliminary look, it is clear that specialisation and distribution of tasks already occur deep within individual sectors. This is hardly surprising, as the functional differentiation of a system such as Austria’s higher education landscape reflects the evolving needs of academic teaching and research. Such developments advance particularly quickly in times of technological change and profound societal transformation. From a state policy perspective, the challenge should be to respond to these needs as proactively and flexibly as possible.

Against this backdrop, the question arises as to whether the strongly segmented sectoral structure – the result of higher education reforms around twenty years ago – remains appropriate today. Only a more detailed empirical analysis could offer an answer, though that would lie beyond FORWIT’s remit. One possible hypothesis is that new needs tend to be met through the founding of new institutions. Yet, considering the current public debate, which often highlights the already large number of higher education institutions in Austria, this appears to be seen as a less than ideal outcome.

As mentioned in a previous blog post, every higher education institution in Austria is clearly assigned to one of four sectors: there are universities of applied sciences, public universities, teacher training colleges (some private, some public), and private universities and colleges (Table 1). Each of these sectors is regulated by its own law: the University Act 2002 (for public universities), the University of Applied Sciences Act, the Higher Education Act 2005 (on the organization of teacher training colleges) and the Private Higher Education Act. There are also some cross-sector regulations, such as the Research Organization Act (FOG) and the Higher Education Quality Assurance Act. The strategic management of this differentiated higher education system is carried out via the Higher Education Plan 2030, which is referred to as “umbrella strategy.” In addition, each of the three sectors directly are under state supervision and also have their own strategy document in the form of multi-year development plans.

The public university sector is the largest, which is apparent from the fact that around 72% of all students study here. 16% of students attend universities of applied sciences, while private universities and teacher training colleges each account for 6% of the total. Regardless of this distribution, however, the legally defined division allows for a sectoral comparison in four dimensions of state regulation, which are borrowed from the EUA’s “University Autonomy Scorecard”: organizational characteristics as defined in the respective legal acts (Table 2); the financing of higher education institutions (Table 3); the regulations governing human resources management at higher education institutions (Table 4); and the framework conditions for the organization of studies by sector (Table 5). Let’s take a closer look at the results.

Table 2 shows, that the legal requirements stipulate that universities of applied sciences and private universities must be organized under private law. Public universities are legally defined as legal entities under public law. Teacher training colleges are federal institutions. The internal governance structure varies from sector to sector, but with the exception of teacher training colleges, they are all variations of “New Public Management”: university management (e.g., rectorate, executive board) is given a central position, usually supported by a kind of supervisory board. In some sectors, collegial bodies (e.g., senate) are also established, especially for the teaching organisation. In the case of public universities, the interface with the state is set in the form of three-year performance agreements (in addition to which there are further reporting obligations for each sector).

The financing of higher education organisations varies from sector to sector, as depicted in Table 3. By far the largest budget, amounting to more than €5 billion, is allocated to public universities; the sectors involving universities of applied sciences and private universities receive less than half a billion euros per year. The significantly higher volume allocated to public universities can be explained not only by the high proportion of students, but also by the fact that they are funded for their research activities, whereas universities of applied sciences are primarily funded for their teaching activities.

Each sector has its own legal framework for human resources management (see Table 4). Universities of applied sciences, public and private universities offer employment under private law (ASVG); at the teacher training colleges, which are federal institutions, employees are civil servants or contract employees of the federal government. Public and private universities each have their own collective wage agreement; however, there is no such agreement for universities of applied sciences. The number of employees (in FTE) varies from sector to sector: with more than 26,000 academic employees, public universities are by far the largest.

Regarding the range of study programs (see Table 5), public universities offer the largest number of programs and cover the widest variety of subjects. Universities of applied sciences provide practice-oriented degree programs that include at least one mandatory internship semester. Teacher education colleges are responsible for teacher training and offer bachelor’s and master’s programs for the primary and secondary education levels, with secondary level teacher training is conducted in cooperation with public universities. Private universities are usually specialized in only a few fields of study. Only public and private universities are authorized to award doctoral degrees.

In summary, each law specified for control purposes has a normative effect on individual higher education institutions in each of the four dimensions. In contrast, there are only a few intersectional norms. Even the overarching control is only understood as an umbrella strategy. This means that higher education institutions are anchored in their respective sectors and are also subject to their own control logic: the Austrian higher education system can be described as highly compartmentalized. Finally, however, it should be noted that focusing exclusively on sectoral differences runs the risk of overlooking further distinguishing features between higher education institutions within a sector. In view of the considerable difference in size between public universities and the other three sectors, an upcoming article will address primarily the country’s 23 public universities.