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Patents and P2: Innovation and Technology Adoption for Environmental Improvements

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Abstract

In this paper, we identify factors that influence adoption of two types of environmental innovations, environmental patents and pollution prevention (P2) activities, and then measure the resulting influence of each on the pollution profile of the firm. We find that environmental patenting is most strongly driven by the technological capacity of the firm, including prior environmental patenting and prior P2 adoptions. While P2 activities are also influenced by prior adoptions of P2, other factors play an important role, including environmental innovation opportunities, the regulatory environment, and firm-specific characteristics. In terms of environmental outcomes, we find that both environmental technologies reduce pollution. Due to strong knowledge accumulation effects of environmental patents, the long-term impact of an environmental patent is stronger and longer lasting than the long-term impact of a P2 practice.

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Notes

  1. Despite the limited literature that deals directly with joint adoption of P2 practices and patents, there is work related to joint adoption of voluntary environmental actions (like P2) and environmental innovations (like a patent). For example, Brouhle et al. (2013) and Lim and Prakash (2014) find positive relationships between participating in a voluntary program (Climate Wise and ISO 14001) and firm patents. Carrion-Flores et al. (2013), on the other hand, find a negative relationship between voluntary 33/50 program participation and environmental patents. Other work (see Cleff and Rennings 2000; Damanpour and Gopalkirshnan 2001; Wagner 2007; Rehfeld et al. 2007; Ziegler and Nogareda 2009; Ozusaglam et al. 2018; Horbach et al. 2012) explores the relationship between self-reported product innovations, process innovations and organizational innovations (such as environmental management systems).

  2. A number of studies consider the impact of environmental patents on a measure of environmental performance in relation to productivity, including Ghisetti and Quatraro (2017) and Weina et al. (2016).

  3. In a similar way, choosing \(S\) and \(P\) (which effectively determines \(X)\) define output, through the function \(f\).

  4. The channels that capture the full effects that account for inter-related patent and P2 decisions can be shown using the expression as follows: \(\frac{dQ}{dP}={g}_{K}\left(\frac{\partial K}{\partial P}\right)+{g}_{X}\left(\frac{\partial X}{\partial P}\right); \frac{dQ}{dS}={g}_{K}\left(\frac{\partial K}{\partial S}\right)+{g}_{X}\left(\frac{\partial X}{\partial S}\right)\). The first term in each expression captures the efficiency enhancing channels through which patents and P2 increase the knowledge stock which decreases pollution. The last term in each expression captures how each technology affects raw material input use.

  5. We aggregate data from facility to the parent company level data using the CUSIP code in the TRI and the GVKEY for patents. We obtain both CUSIP and GVKEY identifiers from the Center for Research in Security Prices (CRSP) and merged the TRI and patent database.

  6. The facilities required to report to the TRI belong to specific industries (NAICS), meet a minimum size (full time employee equivalents) and meet minimum quantity of manufacture/import/process/use of any EPCRA section 313 chemicals (US EPA 2019).

  7. See USEPA: https://www.epa.gov/system/files/documents/2022-02/tri-chemical-list-changes_2.23.22.pdf.

  8. Restricting our analysis to the same set of chemicals allows us to focus on the innovative efforts and toxic releases associated with a fixed set of chemicals. While including newly added or dropping newly deleted chemicals may seem attractive, doing so changes the set of chemicals for which P2 and releases are reported. As a result, it is challenging to know whether measured changes in innovative efforts or releases reflect actual changes in innovative efforts or releases. If we observe changes in our measure of innovation (P2) or changes in our measure of releases, we cannot verify how much of the change is due to chemical substitution or due to changes in reporting requirements. The latter case is particularly problematic because a firm may keep the same P2 practices or keep releases constant over time but would cause our measure to increase or decrease if the chemical is newly added or deleted to the list of required chemicals. For these reasons, we restrict our analysis to the same set of chemicals and recognize that the adoption and innovation–pollution relationship we observe is for these fixed set of substances.

  9. Information on such cross-listing can be found at: https://www.epa.gov/epcra/consolidated-list-lists.

  10. The Green Inventory was developed by the IPC Committee of Experts in order to facilitate patent information searches related to Environmentally Sound Technologies, as listed by the United Nations Framework Convention on Climate Change (UNFCCC) (WIPO 2021).

  11. For instance, if one chemical is reported by all 100 facilities of a parent company, it will be counted as 100 since that captures and controls for the number of opportunities for innovative activity. The number of chemicals can be large for some firms due to the scope of their operations and the number of facilities of the firm. The number of chemicals is highly skewed: the mean is 11,509 and median is 3200 (min is 39, max is 289,559). The number of facilities is also skewed: the mean is 32 and median is 8 (min is 1, max is 912).

  12. Our industry representation is within 5 percentage point of the representation in the TRI with the exception of Other Metals (SIC 34) (under-represented) and Construction (SIC 35) and Electronics (SIC 36) (over-represented).

  13. Following Cameron and Trivedi (2010), the marginal effect can be calculated using the formula \(\varepsilon \times \frac{x}{y}\), where \(\varepsilon\) is the elasticity, \(x\) is the sample mean value of the independent variable of interest, and \(y\) is the predicted value of the dependent variable evaluated at the mean of the regressors. In this case, \(0.9810\times \frac{4.65}{4.82}=0.95\).

  14. Final good has been used as proxy for consumer pressure in other studies that explain environmental behavior or performance (Khanna and Damon 1999; Anton et al. 2004; Vidovic and Khanna 2007; Harrington 2012). In our sample of firms, the following 4-digit SIC codes are considered final goods: 1311, 2000, 2011, 2020, 2050, 2070, 2100, 2200, 2211, 2300, 2330, 2761, 2834, 2840, 2844, 3661, 3669, 3911, 3949, 3950, 4813, 5040, 5140, and 6141.

  15. The form of the instruments matches the form as the environmental patent variable. When the environmental patent variable is expressed in the form of a flow (stock) variable, the instruments are also expressed as flow (stock) variable.

  16. We note that our main results are also robust to exclusion of our initial condition variables, Initial P2 dummy and 1991 P2, which capture unobserved firm heterogeneity. In the patent literature, these measures are frequently included to capture unobserved firm specific characteristics. A closely related paper in the environmental patenting literature (Carrion-Flores and Innes 2010) does not include such pre-sample measures. Exclusion of initial P2 adoption makes R&D Intensity, Peer P2, and Debt to Asset Ratio statistically significant but our core results are the same: we see a positive influence of past P2 and releases on P2 adoption and insignificance of environmental patents on P2 adoption.

  17. Following the form of the dependent and independent variable in our Poisson model, the elasticity (ε) is simply the coefficient estimate on the variable of interest (α) times the same mean value of the variable x (P2 or patent), ε = α*x. In this case, ε = 0.0090 × 4.620452 = 0.04. Since the marginal effect is \(\varepsilon \times \frac{x}{y}\), where \(x\) is the sample mean value of the independent variable of interest, and \(y\) is the predicted value of the dependent variable evaluated at the mean of the regressors, \(0.04\times \frac{\mathrm{9,22}}{8.17}=0.05\).

  18. We investigate 1-year and 3-year lags of regulatory variables and find that the results are robust to using different lags. Results are available upon request.

  19. We investigate whether it is the current flow or lagged flow of P2 and environmental patents that influence pollution. For both technologies, we find stronger evidence that current flows matter compared to further lagged flows of the variable. Results are available upon request.

  20. The mean Total P2 is 8.2 and mean EnvPatents is 4.8, one extra Total P2 adopted is 12.2% of mean Total P2 while one extra EnvPatent is 20.7% of mean EnvPatents.

  21. Our estimated impact of P2 on chemical releases may be biased if P2 adoption influences whether or not a particular chemical is reported to the TRI at any given year. For example, if P2 activities result in reducing chemical releases below the reporting threshold even though it is not entirely eliminated from the production process, our results may underestimate the pollution reduction attributable to P2. Similarly, if P2 adoption has resulted in substitution of one TRI chemical for a non-TRI chemical, any reduction in chemical releases observed may again be an underestimate of the effect of P2 adoption.

  22. The coefficient of (unlogged) lagged P2 in the P2 equation from Table 3 is converted to an elasticity using mean value of P2 for consistency with interpretation of other coefficients.

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Appendix

Appendix

The empirical results allow us to specify our general expressions in (1) and (2) such that that patents are a function of 1-year lagged patent flow, 2-year lagged patent stock, and 2-year lagged P2 stock (Table 2, Model VI); while P2 is explained by 1-year lagged P2 flow and 1-year lagged patent flow (Table 3, Model I). Pollution is affected by contemporaneous P2 and patents (Table 4, Model II). Denoting current (lagged) EnvPatents as \({P}_{it} \left({P}_{it-1}\right)\), 2-year lagged EnvPatent Stock as \({P}_{it-2}^{stock}\), current (lagged) Total P2 as \({S}_{it}\left({ S}_{it-1}\right)\), 2-year lagged Total P2 Stock as \({S}_{it-2}^{Stock}\), and lagged Total Releases as \({Q}_{it-1}\), we can express these models as follows:

$${P}_{it}=exp\left({a}_{P}+{b}_{P}{Q}_{it-1} +{c}_{P2}{S}_{it-2}^{stock}+{d}_{P1}{P}_{it-1}+{d}_{P2}{P}_{it-2}^{stock}+{{\varvec{e}}}_{{\varvec{P}}}{{\varvec{Z}}}_{{\varvec{it}}}^{{\varvec{P}}}+{{\varvec{\eta}}}_{{\varvec{i}}}^{{\varvec{P}}}+{{\varvec{v}}}_{{\varvec{t}}}+{\varepsilon }_{it}^{P}\right)$$
(A.1)
$${S}_{it}=exp\left({a}_{S} +{b}_{S}{Q}_{it-1} + {c}_{S}{S}_{it-1} +{d}_{S}{P}_{it-1} +{{\varvec{e}}}_{{\varvec{S}}}{{\varvec{Z}}}_{{\varvec{it}}}^{{\varvec{S}}}+{{\varvec{\eta}}}_{{\varvec{i}}}^{{\varvec{S}}}+{{\varvec{v}}}_{{\varvec{t}}}+{\varepsilon }_{it}^{S}\right)$$
(A.2)
$${Q}_{it}={a}_{Q}+{b}_{Q}{Q}_{it-1}+{c}_{Q}{S}_{it}+{d}_{Q}{P}_{it} +{{\varvec{e}}}_{{\varvec{Q}}}{{\varvec{Z}}}_{{\varvec{it}}}^{{\varvec{Q}}}+{\eta }_{i}^{Q}+{{\varvec{v}}}_{{\varvec{t}}}+{\varepsilon }_{it}^{Q}$$
(A.3)

In the derivations that follow, we simplify the notation when calculating marginal effects (as elasticities) using count models. For example, we denote \(\frac{\partial {P}_{t+1}}{\partial {P}_{t}}=\frac{\partial log\left[E({P}_{t+1}|Q,K,Z)\right]}{\partial {P}_{t}}\), and \(\frac{\partial {S}_{t+1}}{\partial {S}_{t}}=\frac{\partial log\left[E({S}_{t+1}|Q,K,Z)\right]}{\partial {S}_{t}}\) .  We use elasticities implied by estimates in Tables 2 and 3.

To calculate the effect of an extra environmental patent adopted at time t on pollution τ periods hence, we take the derivative of Eq. (3′) with respect to \({P}_{t}\). We note that pollution at any period is a function of lagged pollution which is in itself a function of \({P}_{t}\) and \({S}_{t}\), and of \({P}_{t+\tau }\) and \({S}_{t+\tau }\), that are themselves functions of \({P}_{t}\) and \({S}_{t}\).

$$\begin{aligned} \frac{{dQ_{t + \tau } }}{{dP_{t} }} & = \underbrace {{\frac{{\partial Q_{t + \tau } }}{{\partial Q_{t + \tau - 1} }}\left[ {\frac{{\partial Q_{t + \tau - 1} }}{{\partial Q_{t + \tau - 2} }} \ldots \frac{{\partial Q_{t} }}{{\partial P_{t} }}} \right]}}_{pollution\;path\;dependence} \\ & \quad + \underbrace {{\frac{{\partial Q_{t + \tau } }}{{\partial P_{t + \tau } }}\left[ {\overbrace {{\left( {\frac{{\partial P_{t + \tau } }}{{\partial P_{t + \tau - 1} }}\frac{{\partial P_{t + \tau - 1} }}{{\partial P_{t + \tau - 2} }} \ldots \frac{{\partial P_{t + 1} }}{{\partial P_{t} }}} \right)}}^{via\;lagged\;patent} + \overbrace {{\left( {\frac{{\partial P_{t + \tau } }}{{\partial P_{t + \tau - 2}^{stock} }}\frac{{\partial P_{t + \tau - 2}^{stock} }}{{\partial P_{t} }}} \right)}}^{via\;patent\;stock} + \overbrace {{\left( {\frac{{\partial P_{t + \tau } }}{{\partial P_{t + \tau - 1} }}\frac{{\partial P_{t + \tau - 1} }}{{\partial P_{t + \tau - 2} }} \ldots \frac{{\partial P_{t + 1} }}{{\partial Q_{t} }}\frac{{\partial Q_{t} }}{{\partial P_{t} }}} \right)}}^{adjustment\;factor}} \right]}}_{pollution\;change\;due\;to\;patent\;knowledge\;accumulation\;from\;extra\;patent} \\ & \quad + \underbrace {{\frac{{\partial Q_{t + \tau } }}{{\partial S_{t + \tau } }}\left[ {\overbrace {{\left( {\frac{{\partial S_{t + \tau } }}{{\partial S_{t + \tau - 1} }}\frac{{\partial S_{t + \tau - 1} }}{{\partial S_{t + \tau - 2} }} \ldots \frac{{\partial S_{t + 1} }}{{\partial P_{t} }}} \right)}}^{via\;lagged\;P2} + \overbrace {{\left( {\frac{{\partial S_{t + \tau } }}{{\partial S_{t + \tau - 1} }}\frac{{\partial S_{t + \tau - 1} }}{{\partial S_{t + \tau - 2} }} \ldots \frac{{\partial S_{t + 1} }}{{\partial Q_{t} }}\frac{{\partial Q_{t} }}{{\partial P_{t} }}} \right)}}^{adjustment\;factor}} \right]}}_{pollution\;change\;due\;to\;P2\;knowledge\;accumulation\;from\;extra\;patent} \\ \end{aligned}$$

The first term illustrates how the trajectory of pollution is lower due to patenting. The second term is the same-knowledge accumulation effect that results from most recent new patent and further lagged patent knowledge stock. The third term is the cross-knowledge accumulation effect because past patenting affects new P2 which in turn affects pollution. Note that for the second and third terms the learning effects include an adjustment factor arising from how the effect of an initial patent on pollution can encourage or discourage future patenting and future P2. Because patenting occurring in the initial period can lower pollution immediately, \(\frac{\partial {Q}_{t}}{\partial {P}_{t}}<0\), the adjustment terms will reinforce (mitigate) the knowledge accumulation effects if lower pollution in the adoption year will encourage (discourage) subsequent patenting and P2, i.e., \(\frac{\partial {P}_{t+1}}{\partial {Q}_{t}}<0;\; \frac{\partial {S}_{t+1}}{\partial {Q}_{t}}<0\) (\(\frac{\partial {P}_{t+1}}{\partial {Q}_{t}}>0;\; \frac{\partial {S}_{t+1}}{\partial {Q}_{t}}>0\)). All these effects will be felt 2 years after adoption because of the 2-year lagged effect of patent stock on new patent flow).

In the year of adoption, τ = 0, the only effect is through the contemporaneous impact which is the focus of Sect. 5.2, \(\frac{d{Q}_{t}}{d{P}_{t}}={d}_{Q}\).

One year after adoption at time t + 1, the effect of patenting at time t is:

$$\begin{aligned} \frac{{dQ_{t + 1} }}{{dP_{t} }} & = \frac{{\partial Q_{t + 1} }}{{\partial Q_{t} }}\left[ {\frac{{\partial Q_{t} }}{{\partial P_{t} }}} \right] + \frac{{\partial Q_{t + 1} }}{{\partial P_{t + 1} }}\left[ {\left( {\frac{{\partial P_{t + 1} }}{{\partial P_{t} }}} \right) + \left( {\frac{{\partial P_{t + 1} }}{{\partial P_{t - 1}^{stock} }}\frac{{\partial P_{t - 1}^{stock} }}{{\partial P_{t - 1} }}} \right) + \left( {\frac{{\partial P_{t + 1} }}{{\partial Q_{t} }}\frac{{\partial Q_{t} }}{{\partial P_{t} }}} \right)} \right] \\ & \quad + \frac{{\partial Q_{t + 1} }}{{\partial S_{t + 1} }}\left[ {\left( {\frac{{\partial S_{t + 1} }}{{\partial P_{t} }}} \right) + \left( {\frac{{\partial S_{t + 1} }}{{\partial Q_{t} }}\frac{{\partial Q_{t} }}{{\partial P_{t} }}} \right)} \right] \\ \end{aligned}$$

Since we are interested in a new patent adopted at time t, not any earlier, several terms are zero. By substituting for all coefficients from (1′), (2′) and (3′):

$$\frac{{dQ_{t + 1} }}{{dP_{t} }} = b_{Q} d_{Q} + d_{Q} \left[ {d_{P1} + 0 + 0} \right] + c_{Q} \left[ {d_{S} + b_{S} d_{Q} } \right] = b_{Q} d_{Q} + d_{Q} d_{P1} + { }c_{Q} \left[ {d_{S} + b_{S} d_{Q} } \right]$$
(A.4)

Two years after adoption at time t + 2, the effect of patenting at time t is derived similarly, noting that new patenting at time t + 2 now also influenced by the effect of 2-year lagged patent stock, \({P}_{t}^{stock}\) and the adjustment terms are non-zero.

$$\begin{aligned} \frac{{dQ_{t + 2} }}{{dP_{t} }} & = \frac{{\partial Q_{t + 2} }}{{\partial Q_{t - 1} }}\left[ {\frac{{\partial Q_{t - 1} }}{{\partial Q_{t} }}\frac{{\partial Q_{t} }}{{\partial P_{t} }}} \right] \\ & \quad + \frac{{\partial Q_{t + 2} }}{{\partial P_{t + 2} }}\left[ {\left( {\frac{{\partial P_{t + 2} }}{{\partial P_{t + 1} }}\frac{{\partial P_{t + 1} }}{{\partial P_{t} }}} \right) + \left( {\frac{{\partial P_{t + 2} }}{{\partial P_{t}^{stock} }}\frac{{\partial P_{t}^{stock} }}{{\partial P_{t} }}} \right) + \left( {\frac{{\partial P_{t + 2} }}{{\partial P_{t + 1} }}\frac{{\partial P_{t + 1} }}{{\partial Q_{t} }}\frac{{\partial Q_{t} }}{{\partial P_{t} }}} \right)} \right] \\ & \quad + \frac{{\partial Q_{t + 2} }}{{\partial S_{t + 2} }}\left[ {\left( {\frac{{\partial S_{t + 2} }}{{\partial S_{t + 1} }}\frac{{\partial S_{t + 1} }}{{\partial P_{t} }}} \right) + \left( {\frac{{\partial S_{t + 2} }}{{\partial S_{t + 1} }}\frac{{\partial S_{t + 1} }}{{\partial Q_{t} }}\frac{{\partial Q_{t + 1} }}{{\partial P_{t} }}} \right)} \right] \\ \end{aligned}$$

By substituting for all coefficients from (1′), (2′) and (3′) and noting that depreciated cumulated stock of patents can be expressed as \({P}_{it}^{stock}=\sum_{\tau }^{t}{\delta }^{\tau }{P}_{t-\tau }\):

$$\frac{{dQ_{t + 2} }}{{dP_{t} }} = b_{Q}^{2} d_{Q} + d_{Q} \left[ {d_{P1}^{2} + d_{P2} \delta^{0} + d_{P1} b_{P} d_{Q} } \right] + { }c_{Q} \left[ {c_{S} d_{S} + c_{S} b_{S} d_{Q} } \right]$$
(A.5)

Generalizing (A.1.2) for all years \(\tau \ge 1\), we have

$$\frac{{dQ_{t + \tau } }}{{dP_{t} }} = b_{Q}^{\tau } d_{Q} + d_{Q} \left[ {d_{P1}^{\tau } + d_{P2} \delta^{\tau - 2} + d_{p1}^{\tau - 1} b_{P} d_{Q} } \right] + { }c_{Q} \left[ {c_{S}^{\tau - 1} d_{S} + c_{S}^{\tau - 1} b_{S} d_{Q} } \right]$$
(A.6)

After multiplying (8) by \(\widehat{P}\), which represents what one extra patent is as a proportion to mean patent, we derive Eq. (4) in Sect. 5.3.

The effect of an extra P2 adopted at time t on future emissions can be analyzed similarly. To calculate the effect of an extra P2 adopted at time t on pollution τ periods hence, we take the derivative of Eq. (3′) with respect to \({S}_{t}\), recognizing that pollution at any period is a function of lagged pollution which is in itself a function of \({P}_{t}\) and \({S}_{t}\), and of \({P}_{t+\tau }\) and \({S}_{t+\tau }\), that are themselves functions of \({P}_{t}\) and \({S}_{t}\).

$$\begin{aligned} \frac{{dQ_{t + \tau } }}{{dS_{t} }} & = \underbrace {{\frac{{\partial Q_{t + \tau } }}{{\partial Q_{t + \tau - 1} }}\left[ {\frac{{\partial Q_{t + \tau - 1} }}{{\partial Q_{t + \tau - 2} }} \ldots \frac{{\partial Q_{t} }}{{\partial S_{t} }}} \right]}}_{pollution\;path\;dependence} \\ & \quad + \underbrace {{\frac{{\partial Q_{t + \tau } }}{{\partial S_{t + \tau } }}\left[ {\overbrace {{\left( {\frac{{\partial S_{t + \tau } }}{{\partial S_{t + \tau - 1} }}\frac{{\partial S_{t + \tau - 1} }}{{\partial S_{t + \tau - 2} }} \ldots \frac{{\partial S_{t + 1} }}{{\partial S_{t} }}} \right)}}^{via\;lagged\;P2} + \overbrace {{\left( {\frac{{\partial S_{t + \tau } }}{{\partial S_{t + \tau - 1} }}\frac{{\partial S_{t + \tau - 1} }}{{\partial S_{t + \tau - 2} }} \ldots \frac{{\partial S_{t + 1} }}{{\partial Q_{t} }}\frac{{\partial Q_{t} }}{{\partial S_{t} }}} \right)}}^{adjustment\;factor}} \right]}}_{pollution\;change\;due\;to\;P2 \;knowledge\;accumulation\;from\;extra\;P2} \\ & \quad + \underbrace {{\frac{{\partial Q_{t + \tau } }}{{\partial P_{t + \tau } }}\left[ {\overbrace {{\left( {\frac{{\partial P_{t + \tau } }}{{\partial P_{t + \tau - 1} }}\frac{{\partial P_{t + \tau - 1} }}{{\partial P_{t + \tau - 2} }} \ldots \frac{{\partial P_{t + 2} }}{{\partial S_{t}^{stock} }}\frac{{\partial S_{t}^{stock} }}{{\partial S_{t} }}} \right)}}^{via\;lagged\;patent} + \overbrace {{\left( {\frac{{\partial P_{t + \tau } }}{{\partial P_{t + \tau - 2}^{stock} }}\frac{{\partial P_{t + \tau - 2}^{stock} }}{{\partial P_{t + 2} }}\frac{{\partial P_{t + 2} }}{{\partial S_{t}^{stock} }}\frac{{\partial S_{t}^{stock} }}{{\partial S_{t} }}} \right)}}^{via\;patent\;stock} + \overbrace {{\left( {\frac{{\partial P_{t + \tau } }}{{\partial P_{t + \tau - 1} }}\frac{{\partial P_{t + \tau - 1} }}{{\partial P_{t + \tau - 2} }} \ldots \frac{{\partial P_{t + 1} }}{{\partial Q_{t} }}\frac{{\partial Q_{t} }}{{\partial S_{t} }}} \right)}}^{adjustment\;factor}} \right]}}_{pollution\;change\;due\;to\;patent\;knowledge\;accumulation\;from\;extra\;P2} \\ \end{aligned}$$

All expressions are analogous to the expression for patents. The first term illustrates how the trajectory of pollution is lower due to an additional P2 practice. The second term is the same-knowledge accumulation effect that results from most recent new P2. The third term is the cross-knowledge accumulation effect because P2 affects patenting which then affects pollution. There are again adjustment factors in the second and third terms arising from how the effect of an initial P2 on pollution can encourage or discourage future P2 and future patenting. Because P2 occurring in the initial period lowers pollution immediately, \(\frac{\partial {Q}_{t}}{\partial {S}_{t}}<0\), the adjustment terms will reinforce (mitigate) the knowledge accumulation effects if lower pollution in the adoption year will encourage (discourage) subsequent patenting and P2, i.e., \(\frac{\partial {P}_{t+1}}{\partial {Q}_{t}}<0;\; \frac{\partial {S}_{t+1}}{\partial {Q}_{t}}<0\) (\(\frac{\partial {P}_{t+1}}{\partial {Q}_{t}}>0;\; \frac{\partial {S}_{t+1}}{\partial {Q}_{t}}>0\)). All these effects will be felt 4 years after adoption because of the 2-year lagged effect of patent stock on new patent flow,  the 2-year lagged effect of P2 stock on new P2 flow, and the 2-year lagged effect of P2 stock on patents.

Differentiating Eq. (3′) with respect to \({S}_{t}\), on the year of adoption, the only effect we can see is the immediate effect, \(\frac{d{Q}_{t}}{d{S}_{t}}={c}_{Q}\).

One year after adoption at time t + 1, the effect of an extra P2 adopted at time t is:

$$\begin{aligned} \frac{{dQ_{t + 1} }}{{dS_{t} }} & = \frac{{\partial Q_{t + 1} }}{{\partial Q_{t} }}\left[ {\frac{{\partial Q_{t} }}{{\partial S_{t} }}} \right] + \frac{{\partial Q_{t + 1} }}{{\partial S_{t + 1} }}\left[ {\left( {\frac{{\partial S_{t + 1} }}{{\partial S_{t} }}} \right) + \left( {\frac{{\partial S_{t + 1} }}{{\partial Q_{t} }}\frac{{\partial Q_{t} }}{{\partial S_{t} }}} \right)} \right] \\ & \quad + \frac{{\partial Q_{t + 1} }}{{\partial P_{t + 1} }}\left[ {\left( {\frac{{\partial P_{t + 1} }}{{\partial S_{t - 1}^{stock} }}\frac{{\partial S_{t - 1}^{stock} }}{{\partial S_{t - 1} }}} \right) + \left( {\frac{{\partial P_{t + 1} }}{{\partial P_{t - 1}^{stock} }}\frac{{\partial P_{t - 1}^{stock} }}{{\partial P_{t - 1} }}\frac{{\partial P_{t - 1} }}{{\partial S_{t - 3}^{stock} }}\frac{{\partial S_{t - 3}^{stock} }}{{\partial S_{t - 3} }}} \right)} \right. \\ & \quad \left. { + \left( {\frac{{\partial P_{t + 1} }}{{\partial Q_{t} }}\frac{{\partial Q_{t} }}{{\partial S_{t} }}} \right)} \right] \\ \end{aligned}$$

Substituting the coefficients from (1′), (2′), the expression at t + 1 is:

$$\begin{aligned} \frac{{dQ_{t + 1} }}{{dS_{t} }} & = b_{Q} c_{Q} + c_{Q} \left[ {c_{S} + b_{S} c_{Q} } \right] + d_{Q} \left[ {0 + 0 + b_{P} c_{Q} } \right] \\ & = b_{Q} c_{Q} + c_{Q} \left[ {c_{S} + b_{S} c_{Q} } \right] + d_{Q} b_{P} c_{Q} \\ \end{aligned}$$
(A.7)

Two and three years after adoption, the general expression can be extended to incorporate longer pollution path dependence effects and longer adjustment in the P2 knowledge accumulation term. We are also able to capture the first expression in the third term. The second expression in the third bracket will still be zero because we are interested in an extra P2 at time t and not any earlier.

$$\begin{aligned} \frac{{dQ_{t + 2} }}{{dS_{t} }} & = \frac{{\partial Q_{t + 2} }}{{\partial Q_{t} }}\left[ {\frac{{\partial Q_{t + 1} }}{{\partial Q_{t} }}\frac{{\partial Q_{t} }}{{\partial S_{t} }}} \right] + \frac{{\partial Q_{t + 2} }}{{\partial S_{t + 2} }}\left[ {\left( {\frac{{\partial S_{t + 2} }}{{\partial S_{t + 1} }}\frac{{\partial S_{t + 1} }}{{\partial S_{t} }}} \right) + \left( {\frac{{\partial S_{t + 2} }}{{\partial S_{t + 1} }}\frac{{\partial S_{t + 1} }}{{\partial Q_{t} }}\frac{{\partial Q_{t} }}{{\partial S_{t} }}} \right)} \right] \\ \quad + { }\frac{{\partial Q_{t + 2} }}{{\partial P_{t + 2} }}\left[ {\left( {\frac{{\partial P_{t + 2} }}{{\partial S_{t}^{stock} }}\frac{{\partial S_{t}^{stock} }}{{\partial S_{t} }}} \right) + \left( {\frac{{\partial P_{t + 2} }}{{\partial P_{t}^{stock} }}\frac{{\partial P_{t}^{stock} }}{{\partial P_{t} }}\frac{{\partial P_{t} }}{{\partial S_{t - 2}^{stock} }}\frac{{\partial S_{t - 2}^{stock} }}{{\partial S_{t - 2} }}} \right) + \left( {\frac{{\partial P_{t + 2} }}{{\partial P_{t + 1} }}\frac{{\partial P_{t + 1} }}{{\partial Q_{t} }}\frac{{\partial Q_{t} }}{{\partial S_{t} }}} \right)} \right] \\ \frac{{dQ_{t + 3} }}{{dS_{t} }} & = \frac{{\partial Q_{t + 3} }}{{\partial Q_{t + 2} }}\left[ {\frac{{\partial Q_{t + 2} }}{{\partial Q_{t + 1} }}\frac{{\partial Q_{t + 1} }}{{\partial Q_{t} }}\frac{{\partial Q_{t} }}{{\partial S_{t} }}} \right] + \frac{{\partial Q_{t + 3} }}{{\partial S_{t + 3} }}\left[ {\left( {\frac{{\partial S_{t + 3} }}{{\partial S_{t + 2} }}\frac{{\partial S_{t + 2} }}{{\partial S_{t + 1} }}\frac{{\partial S_{t + 1} }}{{\partial S_{t} }}} \right) + \left( {\frac{{\partial S_{t + 3} }}{{\partial S_{t + 2} }}\frac{{\partial S_{t + 2} }}{{\partial S_{t + 1} }}\frac{{\partial S_{t + 1} }}{{\partial Q_{t} }}\frac{{\partial Q_{t} }}{{\partial S_{t} }}} \right)} \right] \\ & \quad + \frac{{\partial Q_{t + 3} }}{{\partial P_{t + 3} }}\left[ {\left( {\frac{{\partial P_{t + 3} }}{{\partial P_{t + 2} }}\frac{{\partial P_{t + 2} }}{{\partial S_{t}^{stock} }}\frac{{\partial S_{t}^{stock} }}{{\partial S_{t} }}} \right) + \left( {\frac{{\partial P_{t + 3} }}{{\partial P_{t + 1}^{stock} }}\frac{{\partial P_{t + 1}^{stock} }}{{\partial P_{t + 1} }}\frac{{\partial P_{t + 1} }}{{\partial S_{t - 1}^{stock} }}\frac{{\partial S_{t - 1}^{stock} }}{{\partial S_{t - 1} }}} \right)} \right. \\ & \quad \left. { + \left( {\frac{{\partial P_{t + 3} }}{{\partial P_{t + 2} }}\frac{{\partial P_{t + 2} }}{{\partial P_{t + 1} }}\frac{{\partial P_{t + 1} }}{{\partial Q_{t} }}\frac{{\partial Q_{t} }}{{\partial S_{t} }}} \right)} \right] \\ \end{aligned}$$

Substituting the coefficients from (1′), (2′) and noting that depreciated cumulated stock of P2 can be expressed as \({S}_{it}^{stock}=\sum_{\tau }^{t}{\delta }^{\tau }{S}_{t-\tau }\):

$$\frac{{dQ_{t + 2} }}{{dS_{t} }} = b_{Q}^{2} c_{Q} + c_{Q} \left[ {c_{S}^{2} + c_{S} b_{S} c_{Q} } \right] + d_{Q} \left[ {c_{P2} \delta^{0} + 0 + d_{P} b_{P} c_{Q} } \right]$$
(A.8)
$$\frac{{dQ_{t + 3} }}{{dS_{t} }} = b_{Q}^{3} c_{Q} + c_{Q} \left[ {c_{S}^{3} + c_{S}^{2} b_{S} c_{Q} } \right] + d_{Q} \left[ {d_{P1} c_{P2} \delta^{0} + 0 + d_{P}^{2} b_{P} c_{Q} } \right]$$
(A.9)

Four years after an extra P2 adoption, all terms are non-zero:

$$\begin{aligned} \frac{{dQ_{t + 4} }}{{dS_{t} }} & = \frac{{\partial Q_{t + 4} }}{{\partial Q_{t + 3} }}\left[ {\frac{{\partial Q_{t + 3} }}{{\partial Q_{t + 2} }}\frac{{\partial Q_{t + 2} }}{{\partial Q_{t + 1} }}\frac{{\partial Q_{t + 1} }}{{\partial Q_{t} }}\frac{{\partial Q_{t} }}{{\partial S_{t} }}} \right] + \frac{{\partial Q_{t + 4} }}{{\partial S_{t + 4} }}\left[ {\left( {\frac{{\partial S_{t + 4} }}{{\partial S_{t + 3} }}\frac{{\partial S_{t + 3} }}{{\partial S_{t + 2} }}\frac{{\partial S_{t + 2} }}{{\partial S_{t + 1} }}\frac{{\partial S_{t + 1} }}{{\partial S_{t} }}} \right)} \right. \\ & \quad \left. { + \left( {\frac{{\partial S_{t + 4} }}{{\partial S_{t + 3} }}\frac{{\partial S_{t + 3} }}{{\partial S_{t + 2} }}\frac{{\partial S_{t + 2} }}{{\partial S_{t + 1} }}\frac{{\partial S_{t + 1} }}{{\partial Q_{t} }}\frac{{\partial Q_{t} }}{{\partial S_{t} }}} \right)} \right] + { }\frac{{\partial Q_{t + 4} }}{{\partial P_{t + 4} }} \\ & \quad \left[ {\begin{array}{*{20}c} {\left( {\frac{{\partial P_{t + 4} }}{{\partial P_{t + 3} }}\frac{{\partial P_{t + 3} }}{{\partial P_{t + 2} }}\frac{{\partial P_{t + 2} }}{{\partial S_{t}^{stock} }}\frac{{\partial S_{t}^{stock} }}{{\partial S_{t} }}} \right) + \left( {\frac{{\partial P_{t + 4} }}{{\partial P_{t + 2}^{stock} }}\frac{{\partial P_{t + 2}^{stock} }}{{\partial P_{t + 2} }}\frac{{\partial P_{t + 2} }}{{\partial S_{t}^{stock} }}\frac{{\partial S_{t}^{stock} }}{{\partial S_{t} }}} \right)} \\ { + \left( {\frac{{\partial P_{t + 4} }}{{\partial P_{t + 3} }}\frac{{\partial P_{t + 3} }}{{\partial P_{t + 2} }}\frac{{\partial P_{t + 2} }}{{\partial P_{t + 1} }}\frac{{\partial P_{t + 1} }}{{\partial Q_{t} }}\frac{{\partial Q_{t} }}{{\partial S_{t} }}} \right)} \\ \end{array} } \right] \\ \end{aligned}$$

Substituting the coefficients from (1′), (2′) and noting that \({S}_{it}^{stock}=\sum_{\tau }^{t}{\delta }^{\tau }{S}_{t-\tau }\):

$$\frac{{dQ_{t + 4} }}{{dS_{t} }} = b_{Q}^{4} c_{Q} + c_{Q} \left[ {c_{S}^{4} + c_{S}^{3} b_{S} c_{Q} } \right] + d_{Q} \left[ {d_{P1}^{2} c_{P2} \delta^{0} + d_{P2} \delta^{0} c_{P2} \delta^{0} + d_{P}^{3} b_{P} c_{Q} } \right]$$
(A.10)

Generalizing for all years \(\tau \ge 1\), and simplifying

$$\frac{{dQ_{t + 4} }}{{dS_{t} }} = b_{Q}^{\tau } c_{Q} + c_{Q} \left[ {c_{S}^{\tau } + c_{S}^{\tau - 1} b_{S} c_{Q} } \right] + d_{Q} \left[ {d_{P1}^{\tau - 2} c_{P2} + d_{P2} \delta^{\tau - 4} c_{P2} + d_{P1}^{\tau - 1} b_{P} c_{Q} } \right]$$
(A.11)

After multiplying (13) by \(\widehat{S}\), which represents what one extra P2 is as a proportion to mean P2, we derive Eq. (5) in Sect. 5.3.

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Brouhle, K., Graham, B. & Harrington, D.R. Patents and P2: Innovation and Technology Adoption for Environmental Improvements. Environ Resource Econ 84, 439–474 (2023). https://doi.org/10.1007/s10640-022-00729-3

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