François Perruchas
PhD tutors: Davide Consoli, Nicolò Barbieri
Increasing concern about environmental impact of human activities
⇒ Goal: contain increase in global average temperature below 2ºC
Greenhouse gases (GHG) emissions
↳ Green Innovation needed to...
- decrease emissions (mitigation)
- deal with temperature increases (adaptation)
- protect biodiversity and eco-systems
- accommodate social changes triggered
⇒ Goal: sustainable societies in 50 years
↪ Thesis: Empirical analysis of green innovation
Green technology (GT) is one component among others of "green innovation"
Piecemeal evidence but no comprehensive study of GT over time, across countries, and domains of specialisation
Gaps in the litterature:
Gaps | Contributions |
---|---|
Where and when do GT emerge? | A complete, scalable dataset on GT over time, geographical space and domain of specialisation |
What is the relative state of development across domains of GT? | Heuristic identification of technology life cycle to articulate the physical and social components of GT |
Which factors spur or impede GT development? | Focus on specific location characteristics |
Identify in the patent classification systems (IPC, CPC) codes associated with GT.
OECD Env-tech identifies 4+1 groups:
Number of patent families (x 1000)
Adapted from figure 3.2 (chapter 3)
An extension of Abernathy & Utterback (1978) and Vona & Consoli (2015) to articulate technology life cycle stages through the lenses of the knowledge base
An articulation of 2 concepts:
Stage | Description |
---|---|
Emergence | exploration, experimentation, competition between different designs, highly localised |
Development | standardisation of design, shake out of inferior variants, some diffusion |
Diffusion | stabilisation of design, wider geographical diffusion |
Maturity | dominant design, high standardisation, widest geographical diffusion |
Identification of TLC stages
An example of a technology in diffusion stage
An example of a technology in emergence stage
Group | Technological field | Ranking |
---|---|---|
5 | Capture, storage, sequestration or disposal of GHG | 1 (Less mature) |
6 | CCMT Transportation | 2 |
4 | CCMT Energy generation, transmission or distribution | 3 |
9 | CCMT Production or processing of goods | 4 |
2 | CCMT Water-related adaptation technologies | 5 |
8 | CCMT Wastewater treatment or waste management | 6 |
7 | CCMT Buildings | 7 |
1 | Environmental management | 8 (More Mature) |
Coherent with grey literature, i.e. OECD 2011, Towards Green Growth
Which factors are associated with the production of green patents?
3 perspectives:
Agglomeration economies
Evolutionary economic geography
⇒ Related industries share some cognitive structures that enhance learning opportunities and knowledge spillovers
countries development & capacities
technology complexity
↔
prob. of diversification / specialisation in a technology
based on Petralia et al. (2017), we estimate 2 models :
⇢ probability of diversification in a technology j = to have an RTA without one before
Scjt=β1Densitycjt−1+β2ITCjt+β3Maturityjt+β4GDPct+Controls
⇢ probability of specialization in a technology j = to have an RTA
similar equation but interacting Sizejt, HIjt, ITCjt and Maturityjt with GDPct
with:
- Scjt=1 if RTAcjt > 1
- ITCjt: complexity of a technology (Hidalgo, 2007)
- Densityjt: how close is a technology to country's portfolio
- Maturityjt: life cycle stage of a technology, from 1 to 4
- Controls: HIjt(Herfindhal Index), Sizejt(size of a technology)
Diversification (1) | Diversification (2) | Specialisation | |
---|---|---|---|
Density of neighbouring technologies | 0.02746*** | 0.11723*** | 0.13872*** |
(0.01) | (0.01) | (0.01) | |
Tech-Level Variables | |||
Technology Complexity (ITC) | 0.00169*** | 0.00389*** | 0.00464*** |
(0.00) | (0.00) | (0.00) | |
Maturity | 0.02568*** | 0.04474*** | 0.04034*** |
(0.00) | (0.00) | (0.00) | |
GDP | 0.00012 | 0.00016 | 0.00523*** |
(0.00) | (0.00) | (0.00) | |
GDP x ITC | -0.00008 | ||
(0.00) | |||
GDP x Maturity | 0.00037*** | ||
(0.00) | |||
Controls | Yes | Yes | Yes |
Tech, Country & Time Fixed Effects | Yes | Yes | Yes |
Obs. | 51149 | 70547 | 77065 |
(1) RTA < 0.1 in the previous year, (2) RTA < at the beginning of the sample
territories capacities to recombine knowledge
↔
green/non-green patent families production
We measure variety within the territory knowledge base using patent families and IPC codes (Castaldi, 2015).
3 levels of variety:
Empirical model:
GPLjt=β1UVjt+β2SRVjt+β3RVjt+Controlsjt
Where:
- GPLjt: N. of green patent families per millions inhabitants
- UVjt: Unrelated Variety
- SRVjt: Semi-Related Variety
- RVjt: Related Variety
- Controlsjt: R&D expenditure, % of people with bachelor degree or higher, HC and R&D in neighbour states, Pop. density
Green Pat | All Pat | |
---|---|---|
UV (log) | 1.386*** | -0.931* |
(0.421) | (0.507) | |
SRV (log) | 0.286 | 0.193*** |
(0.184) | (0.0668) | |
RV (log) | 0.392** | 0.515*** |
(0.154) | (0.0987) | |
Controls | YES | YES |
State FE | YES | YES |
Time Dummies | YES | YES |
Random growth | YES | YES |
Obs. | 1466 | 1470 |
Green Pat | Emergence | Development | Diffusion | Maturity | |
---|---|---|---|---|---|
UV (log) | 1.386*** | 0.958* | 1.214** | 0.597 | 0.716 |
(0.421) | (0.523) | (0.590) | (0.786) | (0.473) | |
SRV (log) | 0.286 | -0.356 | 0.783*** | 0.166 | -0.205 |
(0.184) | (0.338) | (0.201) | (0.249) | (0.147) | |
RV (log) | 0.392** | 0.421 | 0.516*** | 0.434* | 0.554*** |
(0.154) | (0.313) | (0.157) | (0.247) | (0.0848) | |
Controls | YES | YES | YES | YES | YES |
State FE | YES | YES | YES | YES | YES |
Time Dummies | YES | YES | YES | YES | YES |
Random growth | YES | YES | YES | YES | YES |
Obs. | 1466 | 1392 | 1371 | 1424 | 1452 |
Task-based framework (Autor, Levy, Murnane, 2003; Autor and Dorn, 2013)
→ focus on relative importance of occupations.
match: main work tasks ↔ skills needed.
Empirical models:
Yj,t=β1GPPj,t−1+β2RIj,t−1+β3GPPj,t−1×RIj,t−1+Controlsj,t
Yj,t=β1GPPj,t−1+β2AIj,t−1+β3GPPj,t−1×AIj,t−1+Controlsj,t
Yj,t=β1GPPj,t−1+β2MIj,t−1+β3GPPj,t−1×MIj,t−1+Controlsj,t
where
- Yj,t: Green Patent stock
- GPPj,t−1: Green Public Procurement
- RIj,t−1=1 if CZ is routine intensive
- AIj,t−1=1 if CZ is abstract intensive
- MIj,t−1=1 if CZ is manual intensive
(I) | (II) | (III) | |
---|---|---|---|
Total GPP | 0.039*** | 0.021** | 0.048*** |
(0.009) | (0.008) | (0.009) | |
Routine Ind. | 0.004 | ||
(0.012) | |||
GPP x RI | -0.000 | ||
(0.011) | |||
Abstract Ind. | 0.017 | ||
(0.015) | |||
GPP x AI | 0.042*** | ||
(0.010) | |||
Manual Ind. | 0.001 | ||
(0.010) | |||
GPP x MI | -0.037*** | ||
(0.012) | |||
controls | YES | YES | YES |
R2 | 0.458 | 0.469 | 0.464 |
N | 3851 | 3851 | 3851 |
Gaps | Contributions | |
---|---|---|
no transversal studies across time, countries, technologies | ✔ | A dataset about green patent families, scalable in space and technologies |
lack of research on dynamics of GT | ✔ | An heuristic articulation of technology life cycle |
lack of research on territory's characteristics correlated with GT development | ✔ | Empirical analysis of territory characteristics that foster green patents production |
Country fitness: average complexity of its technologies
Technology complexity: lower when patented by low-fitness countries
The indicator for each GT is given by the number of countries that exhibit RTA in a particular technology (Petralia et al., 2017; Balland & Rigby, 2017):
UBIQUITYjt=∑cMcj
where Mcj=1 if RTA>1
based on Petralia (2017), we estimate the probability of
⇢ diversification in a technology j
Scjt=Θ1Densitycjt−1+Θ2Densitycjt−1×GDPct+β1logSizejt+β2HIjt+β3ITCjt+δcDc+δjDj+δtDt+εcjt
⇢ specialization in a technology j
Scjt=Θ1Densitycjt−1+β1logSizejt×GDPct+β2HIjt×GDPct+β3ITCjt×GDPct+δcDc+δjDj+δtDt+εcjt
with:
- Scjt=1 if RTAcjt > 1
- ITCjt: complexity of a technology (Hidalgo, 2007)
- Densityjt: how close is a technology to country's portfolio
- HIjt: Herfindhal Index: indicate how concentrated is tech. production
- Sizejt: size of the technology
Div. Eq. (1) | Div. Eq. (2) | Spec. Eq. | |
---|---|---|---|
Density | 0.02746*** | 0.11723*** | 0.13949*** |
(0.01) | (0.01) | (0.01) | |
Density x GDP | 0.00049 | 0.00030 | |
(0.00) | (0.00) | ||
Tech-Level Variables | |||
Log Size | 0.00497** | 0.00658*** | 0.01495*** |
(0.00) | (0.00) | (0.00) | |
Herfindahl Index | -0.01898*** | -0.04997*** | 0.01416* |
(0.01) | (0.01) | (0.01) | |
ITC | 0.00169*** | 0.00389*** | 0.00484*** |
(0.00) | (0.00) | (0.00) | |
GDP | 0.00012 | 0.00016 | 0.00736*** |
(0.00) | (0.00) | (0.00) | |
GDP x Log Size | -0.00049*** | ||
(0.00) | |||
GDP x Herfindahl Index | -0.01453*** | ||
(0.00) | |||
GDP x ITC | -0.00012** | ||
(0.00) | |||
Tech Fixed Effects | Yes | Yes | Yes |
Time Fixed Effects | Yes | Yes | Yes |
Country Fixed Effects | Yes | Yes | Yes |
Obs. | 51149 | 70547 | 77065 |
(1) RTA < 0.1 in the prev. period. (2) RTA < at the beg. of the sample. |
Div. Eq. (1) | Div. Eq. (2) | Spec. Eq. | |
---|---|---|---|
Density | 0.02746*** | 0.11723*** | 0.13872*** |
(0.01) | (0.01) | (0.01) | |
Density x GDP | 0.00049 | 0.00030 | |
(0.00) | (0.00) | ||
Tech-Level Variables | |||
Log Size | 0.00497** | 0.00658*** | 0.01555*** |
(0.00) | (0.00) | (0.00) | |
Herfindahl Index | -0.01898*** | -0.04997*** | 0.00558 |
(0.01) | (0.01) | (0.01) | |
ITC | 0.00169*** | 0.00389*** | 0.00464*** |
(0.00) | (0.00) | (0.00) | |
Maturity | 0.02568*** | 0.04474*** | 0.04034*** |
(0.00) | (0.00) | (0.00) | |
GDP | 0.00012 | 0.00016 | 0.00523*** |
(0.00) | (0.00) | (0.00) | |
GDP x Log Size | -0.00053*** | ||
(0.00) | |||
GDP x Herfindahl Index | -0.01311*** | ||
(0.00) | |||
GDP x ITC | -0.00008 | ||
(0.00) | |||
GDP x Maturity | 0.00037*** | ||
(0.00) | |||
Tech Fixed Effects | Yes | Yes | Yes |
Time Fixed Effects | Yes | Yes | Yes |
Country Fixed Effects | Yes | Yes | Yes |
Obs. | 51149 | 70547 | 77065 |
Diversification probabilities
Specialization probabilities
We measure variety with the territory knowledge base using patent families and IPC codes (Castaldi, 2015).
UVit=∑ksk,itln(1sk,it)
SRVit=∑lsl,itln(1sl,it)−∑ksk,itln(1sk,it)
RVit=∑msm,itln(1sm,it)−∑lsl,itln(1sl,it)
where
- i: Federal State
- k,l,m: Technological categories at IPC 1-digit, 4-digit, 8-digit
- sk,it: Share of patents in category k(l,m)
Level I (k) - UV | Level II (l) - SRV | Level III (m) - RV |
---|---|---|
C Chemistry | C01B Non-metallic elements | C01B 6/10 Monoborane; Diborane; Addition complexes thereof |
.. | .. | C01B 6/11 Preparation from boron or inorganic compounds containing boron and oxygen |
.. | .. | C01B 6/13 Addition complexes of monoborane or diborane, e.g. with phosphine, arsine or hydrazine |
.. | .. | C01B 6/15 Metal borohydrides; Addition complexes thereof |
.. | C01C Ammonia | . |
.. | C01D Lithium, sodium, potassium, etc | . |
.. | C01F Compounds of the metals beryllium, magnesium | . |
D Textiles | .. | .. |
F Mechanical Engineering | .. | .. |
Empirical model:
GPLjt=β1Varietyjt+β2R&Djt+β3HCjt+Controlsjt+τj+γt+δjt+ejt
Where:
- GPLjt: N. of green patent families per millions inhabitants in US states over year
- Varietyjt: UV, SRV and RV
- R&Djt: R&D expenditure
- HCjt: % of people with bachelor degree or higher
- Controlsjt: HC and R&D in neighbour states, Pop. density
- γt: state fixed effects
- δjt: time fixed effects
Many works about the impact of public procurement on innovation (Nelson, 1982; Geroski, 1990; Ruttan, 2006)
↳ Few research on green innovation in particular (Ghisetti, 2017).
GT: double-externality issue (Jaffe, Newell, and Stavins, 2005)
→ Public policy required
Task-based framework (Autor, Levy, Murnane, 2003; Autor and Dorn, 2013)
→ focus on relative importance of occupations.
match: main work tasks ↔ skills needed.
For each type of task (A,R,M)
For each occupation: ATIk=ln(TAk,1980)−ln(TRk,1980)−ln(TMk,1980)
Then, for each CZ j: ASHjt=(∑Kk=1Ljkt⋅1[ATIk>ATIP66])(∑Kk=1Ljkt)−1
Finally, if the CZ j is in the top third of national task share at time t: AIjt=1[ASHjt>ASHP66t]
Using full sample of 722 CZs observed from 2000 to 2011. We investigate the relationship between GPP and local green technological activity:
Y_{j,t}=\beta_{0}+\beta_{1}GPP{}_{j,t-1}+\boldsymbol{X_{j,t}^{\prime}}\beta_{2}+\epsilon_{j,t}
where:
- Y_{j,t}: fractionalized stock of green patent families (weighted by forward citations) at time _{t} filed by inventors resident in CZ _{j};
- GPP{}_{j,t-1}: level of expenditures for GPP performed in CZ _{j} at time _{t−1} (2010 USD);
- X_{j,t}^{\prime}: controls for CZs' labor force and demographic composition
(I) | (II) | |
---|---|---|
product GPP | 0.053*** | |
(0.013) | ||
service GPP | 0.087*** | |
(0.011) | ||
pop density | -0.000 | -0.000 |
(0.000) | (0.000) | |
empl share | -0.000** | -0.000** |
(0.000) | (0.000) | |
N. of Firms | 0.000*** | 0.000*** |
(0.000) | (0.000) | |
R&D empl | 8.609** | 8.510** |
(4.378) | (4.168) | |
R2 | 0.472 | 0.498 |
N | 7937 | 7937 |
Standard errors clustered at the level of State. Models include a constant, year dummies and geographic dummies (9 Census divisions). * p < . 1, ** p < . 05, *** p < . 01
To test for moderating effects of CZ occupational task compositions, we estimate 3 models:
Y_{j,t}=\beta_{0}+\beta_{1}GPP_{j,t-1}+\beta_{2}RI_{j,t-1}+\beta_{3}GPP_{j,t-1}\times RI_{j,t-1}+\boldsymbol{X_{j,t}^{\prime}}\beta_{4}+\epsilon_{j,t}
Y_{j,t}=\beta_{0}+\beta_{1}GPP_{j,t-1}+\beta_{2}AI_{j,t-1}+\beta_{3}GPP_{j,t-1}\times AI_{j,t-1}+\boldsymbol{X_{j,t}^{\prime}}\beta_{4}+\epsilon_{j,t}
Y_{j,t}=\beta_{0}+\beta_{1}GPP_{j,t-1}+\beta_{2}MI_{j,t-1}+\beta_{3}GPP_{j,t-1}\times MI_{j,t-1}+\boldsymbol{X_{j,t}^{\prime}}\beta_{4}+\epsilon_{j,t}
where RI_{j,t-1}, AI_{j,t-1} and MI_{j,t-1} are dummy variables indicating if CZ is routine, abstract or manual intensive tasks respectively.
Due to occupational data availability, we consider here 2005-2011.
(I) | (II) | (III) | (IV) | (V) | (VI) | |
---|---|---|---|---|---|---|
tot GPP | 0.039*** | 0.039*** | 0.039*** | 0.021** | 0.040*** | 0.048*** |
(0.008) | (0.009) | (0.008) | (0.008) | (0.008) | (0.009) | |
RI | 0.003 | 0.004 | ||||
(0.013) | (0.012) | |||||
GPP x RI | -0.000 | |||||
(0.011) | ||||||
AI | 0.041*** | 0.017 | ||||
(0.014) | (0.015) | |||||
GPP x AI | 0.042*** | |||||
(0.010) | ||||||
MI | -0.013 | 0.001 | ||||
(0.010) | (0.010) | |||||
GPP x MI | -0.037*** | |||||
(0.012) | ||||||
controls | YES | YES | YES | YES | YES | YES |
R2 | 0.458 | 0.458 | 0.464 | 0.469 | 0.461 | 0.464 |
N | 3851 | 3851 | 3851 | 3851 | 3851 | 3851 |
GPP, RSH, ASH and MSH lagged 1-year. Standard errors clustered at the level of State. Models include a constant, year dummies and geographic dummies (9 Census divisions). * p < . 1, ** p < . 05, *** p < . 01